neuropsychology of depression and related mood disorders · 222-igrant-chap22.indd...

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� e present chapter follows several previous chapters on depression and depression-related cognitive di� culties by King and Caine (1996) and Caine (1986) in earlier editions of this book. As King and Caine note in the previous edition of this book, study of cognition in mood disorders has moved from being a nuisance when study-ing late-life dementias, also known as pseudo-dementia, to a full & edged area of inquiry. � e underlying assumptions about mood disorders as being functional and not organic disorders have now been assailed on a number of di( erent fronts (Tucker et al., 1990). In fact, it is now best to consider mood disorders under a category of “potentially reversible cognitive decrements” (Sobow et al., 2006), although the careful reader of this chapter will come to appreciate that for some individuals, the cognitive di� culties that co-occur with depression are in no way revers-ible. � is characterization may sit poorly with many in a 1 eld christened in the tradition of degenerative dementias, wherein dysfunction is the inevitable, and currently irreversible, sequelae of neurological disease. However, as studies of disorders such as multiple sclerosis and Huntington’s disease have aptly informed the 1 eld, assumptions about “functional” dis-orders can be largely misplaced (Gha( ar & Feinstein, 2007). � is is by no means to asso-ciate the frequently present, moderate cognitive ine� ciencies observed in depression to those more severe, highly prevalent, and persistent di� culties observed in degenerative condi-tions. Rather, the purpose is to place the 1 eld

Tremendous strides have been made toward understanding the neuropsychological, neuro-anatomical, and neuroimaging 1 ndings associ-ated with depression. Whereas understanding of the neuroanatomical networks involved in depression and related mood disorders remains in an adolescent phase, neuropsychological 1 ndings in depression and related mood disor-ders are fairly well codi1 ed at this point. To be sure, there are a number of clinical and demo-graphic features that substantially impact cog-nitive performance in the context of a mood disorder, including later age of onset, polyphar-macy/substance abuse, medical complications, greater severity, and resistance to traditional treatments. Whereas the “causes” for depres-sion-associated cognitive di� culties are het-erogeneous, the co-occurrence of features of depression and cognitive di� culties of speci1 c types suggests a common set of neural networks that may be adversely a( ected, including medial and ventral frontal, limbic, and basal ganglia structures. � e present chapter is intended to provide the reader with a blend of traditional neuropsychological investigations of depression and related mood disorders, herea< er referred to generally as mood disorders, in addition to integrating the latest cognitive and a( ective neuroscience research. We will review the mod-erating impact of age of onset, e( ort, subtypes of depression, and medications on cognitive func-tioning in mood disorders, as well as research using cognitive measures as predictors of treat-ment response.

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Neuropsychology of Depression and Related Mood Disorders

Scott A. Langenecker, H. Jin Lee, and Linas A. Bieliauskas

22-IGrant-Chap22.indd 122-IGrant-Chap22.indd 1 12/16/2008 7:10:51 PM12/16/2008 7:10:51 PM

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Neuropsychiatric Disorders2

assessment of mood disorders. � e chapter closes with several compelling studies that have used neuropsychological and cognitive/a( ec-tive neuroscience measures to predict treatment response, a summary, and some recommenda-tions for what a “core” battery might be com-prised of when assessing a patient with a mood disorder.

Traditional Studies and Cognitive Pro� le

Major depressive disorder (MDD) is associated with ine� ciencies in various cognitive domains (Austin et al., 2001, p. 57; Miller, 1975; for reviews see Elliott, 1998; Rogers et al., 2004) including attention (Cornblatt et al., 1989; Porter et al., 2003; Weiland-Fiedler et al., 2004), psychomotor speed (Sobin & Sackeim, 1997), executive func-tion (Grant et al., 2001; Paelecke-Habermann et al., 2005), and memory (Austin et al., 1992; Brown et al., 1994; Burt et al., 1995; Elliott et al., 1996). It is well known that intragroup heteroge-neity of cognitive decrements exist, though the reasons are still not completely clear. Possible mediating factors, some of which are addressed later in this chapter, include the age of onset, age of participants, premorbid level of cognitive function, symptom severity, number of previ-ous depressive episodes, e( ort and motivation, medication status, patient status (inpatient or outpatient), state of the individual (i.e., remit-ted or depressed), and history of hospitalization (Elliott, 1998; Fossati et al., 2002; Gualtieri et al., 2006). As such, drawing conclusions regarding cognitive decrements in MDD is challenging because studies o< en utilize a depressed group of heterogeneous individuals varying in afore-mentioned characteristics. Furthermore, many such studies are powered only to detect large and very large e( ect sizes and also typically use tasks designed to assess for obvious brain injury or neurodegenerative conditions.

In this section, we review cognitive decre-ments in adults who are under the age of 65 as other neurological factors o< en contribute in the disease process in geriatric or late-on-set depression. � e speci1 c topic of late-onset depression is discussed in the Age of Onset sec-tion in this chapter. Readers can also 1 nd sev-eral excellent articles on neuropsychological

and the reader in the proper pose of humility when making inferences about the current state of knowledge in brain–behavior relationships in mood disorders. Mood disorders, like any other psychological phenomena, have the central ner-vous system (CNS) as the wellspring for their existence, and many of the same tracts and sys-tems implicated in neurological disorders are similarly implicated in mood and other psychi-atric disorders (Cummings, 1993).

� e burgeoning 1 elds of a( ective and cog-nitive neuroscience, as well as the expanding body of knowledge on mood disorders from traditional neuropsychological investigations, together make a chapter on the neuropsychol-ogy of mood disorders no small endeavor. � ere are many allied areas of inquiry that are not seamlessly integrated into one theory, model, or basis for understanding of the brain–be-havior relationships most typically observed in mood disorders. Given this challenge, we have attempted to balance speci1 c and important areas of inquiry within mood disorders with the more general purview that one might typically expect from a book chapter. We make no pre-tense of being exhaustive in breadth or depth, but do hope that the present chapter provides an ample overview of general areas along with very speci1 c subtopics that we feel the reader might most bene1 t from. We begin with an overview of what might be considered a typical cognitive pro1 le of a depressed patient, based largely on the traditional neuropsychological studies of depression. We move from these to discuss neu-roanatomical and neuroimaging evidence cur-rently available in mood disorders research. � e neuroimaging data largely rely upon cognitive and a( ective neuroscience research. We then delve into critical mediating factors in under-standing brain–behavior relationships in mood disorders. � is is a necessary and, we believe, very valuable overview of factors that directly relate to what extent cognitive di� culties might be expected to occur in mood disorders. We will also highlight some speci1 c subtypes of mood disorders that illustrate potential brain–behavior relationships, or where greater preva-lence or severity of cognitive di� culties might be expected. We review potential medication e( ects in depression, as this is likely a frequent question that arises in the neuropsychological


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Neuropsychology of Depression and Related Mood Disorders 3

di� cult). By way of background, the automatic e( ortful hypothesis states that depressed indi-viduals should incrementally show poorer per-formance as the degree of e( ort required on any given task increases (Hasher & Zacks, 1979). Similarly, Rose and Ebmeier (2006) examined working memory in 20 patients with MDD (in or outpatients, most of who were on antide-pressant medications) and 20 healthy controls by using a di( erent version of n-back task. � e researchers found slower reaction times and reduced accuracy in the MDD group compared to the control group in a linear fashion, again, with no disproportionately slower reaction time and decreased accuracy with increasing cogni-tive load, suggesting a more uniform pattern of weakness rather than an automatic/e( ortful continuum.

In tasks of sustained attention, individuals with MDD were shown to make more omission errors (Langenecker et al., 2007a, 2007b; Porter et al., 2003; Sevigny et al., 2003) and commis-sion errors (Farrin et al., 2003; Porter et al., 2003) on continuous performance tests (CPTs). In addition, individuals who were in euthymic or remitted states were also shown to demon-strate persistent di� culty with sustained atten-tion. Recently, Weiland-Fiedler et al. (2004) found decrements in 28 fully remitted, unmed-icated individuals with a history of MDD com-pared to 23 healthy controls on the Rapid Visual Information Processing Task of the Cambridge Neuropsychological Test Automated Battery (CANTAB). Paelecke-Habermann et al. (2005) also found continued attention di� culty in remitted individuals with MDD. Although the aforementioned studies demonstrated atten-tional ine� ciencies in euthymic and depressed states, Grant et al. (2001) did not 1 nd di( er-ences on the digit span task and a CPT in 123 nonchronic, MDD outpatients and 36 healthy controls. � e dependent measures reported by Grant and colleagues were di( erent from the measures reported by other researchers (Harvey et al., 2004; Porter et al., 2003; Sevigny et al., 2003).

Psychomotor Speed

Research on psychomotor speed in mood dis-orders have shown inconsistent 1 ndings. Some

dysfunction in geriatric depression (Bieliauskas, 1993; Elderkin-� ompson et al., 2007; Marcos et al., 2005; Sheline et al., 2006; Wright & Persad, 2007). We focus on domains of atten-tion, psychomotor speed, executive function, and memory, which are the areas that have been most researched and found to be pertinent to MDD (Bulmash et al., 2006; Gualtieri et al., 2006; Landro et al., 2001; Porter et al., 2003). We also understand that sorting neuropsychologi-cal measures into di( erent constructs may be arbitrary as one measure usually taps more than one cognitive domain. Furthermore, attention, working memory, processing speed, and execu-tive function mainly involve the common struc-tures in the frontal–subcortical neural networks (Cummings, 1995; Mega & Cummings, 1994).

Attention

Research on attention in MDD suggests that although simpler attentional processes may not be a( ected in adult depressed patients (Harvey et al., 2004; Ravnkilde et al., 2003), more com-plex attentional processes such as working memory or sustained attention are o< en com-promised in patients with MDD (Porter et al., 2003; Rose & Ebmeier, 2006) and even in remit-ted individuals in some cases (Weiland-Fiedler et al., 2004). Hartlage et al. (1993) noted that depressed individuals have more di� culty on tasks that require e( ortful processing com-pared to tasks that are simpler or require automatic processing. Indeed, some studies of attentional processing in MDD have shown that short-term attention such as the forward digit span is generally una( ected compared to more di� cult working memory tasks (Harvey et al., 2004; Ravnkilde et al., 2003). For exam-ple, Harvey et al. (2004) found that short-term attention on forward and backward digit span and forward spatial span did not di( er between 22 young inpatients with MDD and 22 normal controls, while poorer performance was found in the patient group on a backward spatial span task and a verbal n-back task. However, their results did not fully support an automatic ver-sus e( ortful processing hypothesis as they failed to 1 nd a group and complexity interac-tion (i.e., the patient group did not do dispro-portionately worse as the task became more


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assessments involve various functions including concept formation, set-shi< ing, planning, inhi-bition, working memory, and & uency (Brown et al., 1994; Miyake et al., 2000). Some of the commonly used tests to assess executive dys-function both in research and clinical settings include the Wisconsin Card Sorting Test (WCST; Channon, 1996; Grant et al., 2001), tests of ver-bal & uency (for a review see Henry & Crawford, 2005), the Stroop interference test (Harvey et al., 2004; Markela-Lerenc et al., 2006), Trail-Making Test Part B (Grant et al., 2001; Harvey et al., 2004), the Tower of London test (Naismith et al., 2006; Porter et al., 2003), and the Halstead–Reitan Category test (Grant et al., 2001).

Individuals with depressive symptoms have been shown to complete fewer categories and make more perseverate responses on the WCST (Channon, 1996; Grant et al., 2001), although inconsistently (Fossati et al., 2001; Ravnkilde et al., 2003). Grant et al. (2001) compared 123 outpatients with MDD to 36 healthy controls on the WCST and found that the MDD group completed a fewer number of categories, made more perseverative responses and errors, and more o< en failed to maintain set and learn. No di( erence was found, however, on the other measures of executive functioning including the Halstead–Reitan Categories Test, letter & u-ency, and Trail-Making Test Part B. Ravnkilde et al. (2003) found no di( erence on the WCST between 40 inpatients with MDD and a group of 49 controls. � e researchers did 1 nd that Stroop word reading, color naming, and interference were all poorer in the MDD group compared to the healthy control group. Harvey et al. (2004) reported that the depressed group did not di( er from the control group on the word reading con-dition of the Stroop test while they performed worse on the color naming and interference conditions. � e researchers also found slower completion times on the Trail-Making Test Part B. Fossati et al. (2001) did not 1 nd a signi1 cant di( erence between 22 depressed inpatients and 22 healthy controls on the modi1 ed WCST but found a di( erence on the spontaneous condi-tion of the California Card Sorting Test (Delis et al., 1992). Individuals with MDD were found to generate fewer words on verbal & uency tasks (i.e., a lexical & uency test and on the ‘exclude letter’ & uency test) in some studies (Porter et al.,

studies have shown slowed psychomotor speed in individuals with mood disorders (Bulmash et al., 2006; Sobin & Sackeim, 1997) while other studies have not found any di( erence between depressed individuals and healthy controls (Grant et al. 2001; Porter et al., 2003). In general, psychomotor slowing has been more commonly associated with melancholic depression (Austin et al., 1999) and/or in older individuals with depression (Beats et al., 1996; Nebes et al., 2000). In medication-free younger individuals, normal performance was found on the Digit Symbol Substitution Test on the Wechsler Adult Intelligence Scale-Revised (WAIS-R) and on a CPT in terms of respond latency (Porter et al., 2003). In addition, Harvey et al. (2004) also found no di( erence between 22 inpatients with MDD and 22 healthy controls on the Trail-Making Test Part A. Perhaps, the level of di� culty or required e( ort has an e( ect on psychomotor slowing. Hammar et al. (2003) examined automatic and e( ortful processing by using a visual search par-adigm in 21 individuals with MDD on antide-pressant medications and healthy controls. � e results indicated no di( erence in reaction time between the two groups on the task that was thought to only require automatic processing (one distractor task). Nevertheless, reaction time was slower in the MDD group when the task required e( ortful processing (two distractor task). Another study of 26 patients with MDD reported positive relationships between choice reaction time on the California Computerized Assessment Package clinical depression rat-ing scales (Egeland et al., 2005). Bulmash et al. (2006) examined 18 unmedicated individuals with MDD and 29 controls on a driving simula-tor for four 30-minute trials throughout the day. � e MDD group demonstrated overall slowed steering reaction time and increased number of crashes compared to the control group even a< er age and sleepiness were controlled.

Executive Functioning

Research has shown that individuals with MDD demonstrate decrements on tasks that are pre-sumed to measure executive function (Grant et al. 2001; Langenecker et al., 2005; Paelecke-Habermann et al., 2005; Porter et al. 2003). Executive function tasks in neuropsychological


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utilized recall versus recognition memory tasks (presuming that the recall tasks are more di� -cult than recognition tasks) to examine Hasher and Zacks’ hypothesis. � e researchers found that the only di( erence between the medica-tion-free MDD group and the normal group on the RAVLT was the distractor list recall with-out any evidence of proactive interference. In contrast, the researchers found that the MDD group performed poorer on measures of recog-nition memory (memory for visual information such as patterns and spatial locations on the CANTAB compared to the control group).

As mentioned, the number of recurrent depressive episodes and inpatient status may be associated with poorer memory function. Basso and Bornstein (1999) studied 20 single-episode depressed inpatients and 46 recurrent depressed inpatients and found a greater memory dysfunc-tion in the recurrent group compared to single-ep-isode patients on the California Verbal Learning Test-Second Edition (CVLT-II) measures includ-ing total initial recall, learning curve, short-de-lay cued recall, long-delay free and cued recall, and recognition discrimination. Ravnkilde et al. (2003) did not 1 nd a di( erence in learning and memory on the Luria Verbal Learning Test but found a di( erence on the immediate recall of the Logical Memory Test of the WAIS and immedi-ate and delayed recall on the Visual Reproduction Test of the WMR-R between 40 medicated depressive inpatients and 49 normal controls. More positively, memory decrements appear to subside in remitted individuals. Weiland-Fiedler et al. (2004) did not 1 nd any di( erence in mem-ory acquisition on the CVLT-II between a group that consisted of 28 fully remitted, unmediated individuals with a history of MDD and a group of 23 healthy participants.

Neuroanatomy of Mood Disorders

Limbic, frontal, and subcortical areas have been strongly implicated in depression and related mood disorders (Alexopoulos, 2002; Carroll et al., 1976; Drevets & Raichle, 1992; Goldapple et al., 2004; Mayberg et al., 1994; Videbech, 2000), although it should be noted that these areas are implicated in a number of disorders for which dysfunctions in mood and a( ect are not always present (Nilsson et al., 2002; Owen, 2004;

2003) but not in others (Harvey et al., 2004; Naismith et al., 2006). No di( erence was found between the MDD and normal controls on the Tower of London test (Naismith et al., 2006; Porter et al., 2003).

Memory

Although many studies have shown decreased memory function in patients with MDD (Austin et al., 1992; Bornstein et al., 1991; Brown et al., 1994; Burt et al., 1995; Elliott et al., 1996), the 1 ndings again do not show consistent patterns or severity of memory impairment (Basso & Bornstein, 1999; Brand et al., 1992; Fossati et al., 2002). In addition, the initial acquisi-tion process seems to be more a( ected than retrieval in depressed individuals, as evidenced by decreased initial recall as well as decreased delayed recall and recognition (Austin et al., 1992; Basso & Bornstein, 1999; Ravnkilde et al., 2003). � is is likely associated with the a( ected attentional processes that are inter-fering with the encoding process. Austin et al. (1992) indicated poorer performance on initial acquisition and delayed recall and recognition portions of the Rey Auditory Verbal Learning Test (RAVLT) in two depressed patient groups (“endogeneous” and “neurotic”) compared to controls. In contrast, Grant et al. (2001) failed to 1 nd di( erences in memory function in 123 MDD outpatients compared to controls on the Hopkins Verbal Learning Test (HVLT) and on the Visual Reproduction subtest of the WAIS-R. Hasher and Zacks (1979) proposed that memory dysfunction in depression is likely demonstrated with more complex tasks that require e( ortful encoding rather than less e( ortful or automatic encoding processes. Rohling and Scogin (1993) examined 30 depressed patients (21 outpatients and 9 inpatients), 20 psychiatric controls, and 30 normal controls on measures that appeared to be more challenging such as paired-associate learning or free recall tasks versus measures that were presumed to be easier such as memory for frequency of occurrence or spatial locations. � e researchers noted that as there were no sig-ni1 cant correlations between the measures of depression severity and the measures of e( ortful memory, their results failed to support Hasher and Zacks’ hypothesis. Porter et al. (2003)


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incorporated within these circuits and are con-sistent with what was previously described in this book by King and Caine (1996).

Structural Imaging

Structural imaging studies of MDD and related mood disorders, like most morphologic studies, are limited primarily by region of interest (ROI) approaches. For example, many studies only examine one ROI within total brain volume, or a subsection of total brain volume. � is approach can provide for a single disassociation, but does not address the possibility of entire systems being disrupted in mood disorders. It does not enable one to understand other parts of the lim-bic and frontal networks that may be implicated in depression, nor does it address whether there is a global problem or if other systems are unaf-fected. More recently, voxel-based approaches have been used to do full brain comparisons, but these are limited by heterogeneity in anat-omy across subjects and limitations in strategies for anatomical warping to address the hetero-geneity. � ere are a number of brain regions implicated in the neuroanatomy of mood disor-ders, illustrated in Figure 22.1.

Van Praag et al., 1975). � e relationship of mood and cognitive disturbance to neuroanatomical abnormalities remains somewhat tenuous, as few studies have examined these relationships with su� cient sample size and measurement speci-1 city. � is section will review several structures shown to be a( ected in mood disorders but will not exhaustively review the many studies that have been completed of morphometry in mood disorders. � ere is speci1 c focus on studies with larger samples, better clinical characterization, and attempts to look at the relationship between cognitive or a( ective variables and brain mor-phometry when possible. Functional imaging studies are reviewed in a subsequent section below. Figure 22.1 illustrates brain regions referenced throughout this section, whereas Brodmann areas can be found elsewhere (Kolb & Whishaw, 1996). (See also the color version in the color insert section.) By and large, these areas are all part of a ventral cingulate–medial prefrontal circuit, as described by Cummings (1993) and further subdivided by Rolls (Rolls, 1999) in lateral and medial orbital-frontal circuits. � ere is also a dorso-lateral prefrontal circuit that is impli-cated in some of the research described below. We further note that subcortical structures are

Regions of Interest in MoodDisorders 1. Insula 2. Dorsolateral Prefrontal 3. Anterior Temporal 4. Orbital Frontal 5. Putamen 6. Hippocampal Formation 7. Amygdaloid Complex 8. Posterior Cingulate 9. Dorsal Anterior Cingulate10. Caudate11. Medial Prefrontal12. Nucleus Accumbens13. Thalamus & Hypothalamus

15. Subgenual Anterior Cingulate

14. Raphe

Figure 22–1. Frequent Regions of Interest Reported in Structural and Imaging Studies Relevant to Understanding Depression and Related Psychiatric Disorders. Numbers indicate center of foci, although some foci are collapsed across the le< -right axis to reduce the number of images necessary to display these foci.


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education matched controls indicated a rela-tionship between right hippocampal volume and persisting memory di� culty 6 months a< er the initial assessment (O’Brien et al., 2004). Of course, it is unknown if smaller hippocampi placed one at greater risk for mood disorders and reduced cognition due to increased vulner-ability, or if these are the result of length and severity of illness.

� ere is continued debate about whether the amygdala is involved in mood disorders, whereas it is well-established that the amyg-dala is critical in anxiety disorders. � ere is a mixture of studies reporting increased as com-pared to decreased amygdala volume (Sheline, 2003). A study of 30 1 rst-episode patients with MDD demonstrated increased bilateral hip-pocampal volume compared to the match control group of 30 patients and 27 recurrent depressed patients and was stable over 1 year (Frodl et al., 2003). A larger study of 47 female twins pairs exploring amygdala volumes sug-gested that there were no di( erences between those with MDD, those at high risk for MDD, and the control subjects, although there was a signi1 cant relationship between twin amygdala volumes suggesting a genetic in& uence (Munn et al., 2007). Reductions in amygdala volumes have been associated with the short form of the 5-HTTLPR polymorphism in one large study of 114 subjects using voxel-based morphometry (Pezawas et al., 2005), but with the long form in another study of 247 young adult female twins (Chorbov et al., 2007). � e data available on relationships between amygdala volume, cog-nition, and a( ect measures is limited in mor-phometry data and may be further complicated by comorbidity between depressed and anxiety subjects in some studies. Whereas there are ani-mal and theoretical models of mood disorders that would suggest an important role for the amygdala in the etiology and maintenance of the mood disorders, the present state of knowledge using morphological techniques is equivocal in this regard. Importantly, there is now some sug-gestion that amygdala volumes may be enlarged in 1 rst onset of mood disorder, yet smaller or not di( erent in size with recurrence of depres-sion and/or treatment with medication.

� e frontal lobes have long been implicated in mood disorders (Harlow, 1868; Moniz, 1954).

� e importance of the hippocampus in mood disorders is now accepted, though the speci1 city and reliability of this relationship has yet to be fully explicated (Caetano et al., 2004; Campbell & MacQueen, 2004; Lee et al., 2002; Lopez et al., 1998; Mervaala et al., 2000; Sapolsky, 2001; Stockmeier et al., 2004). � ere are sev-eral reviews of studies of hippocampal (and amygdalar—see below) volume and depres-sion available and we refer the reader to these (Sheline, 2003; Videbech & Ravnkilde, 2004). A voxel-based morphometry study of 20 patients with treatment resistant depression (TRD), compared to 20 remitted and 20 control sub-jects revealed atrophy in bilateral hippocam-pal structures, as well as other areas described in subsequent sections (Shah et al., 2002). � eir TRD group had a signi1 cantly greater number of hospitalizations compared to the remitted depressives and all had received at least six sessions of electroconvulsive therapy (ECT). An earlier study by this group dem-onstrated reduced Auditory Verbal Learning performance in treatment resistant depression patients compared to remitted patients (Shah et al., 1998) and a positive correlation between le< hippocampal volume and AVLT perfor-mance. A study by Vythlingham and colleagues failed to demonstrate di( erences in hippocam-pal volume between 38 depressed and 33 con-trol subjects, but the study did show trend level correlations between hippocampal volume and visual immediate and visual delayed recall on the Selective Reminding Test (Vythilingam et al., 2004). In 37 moderate to severe traumatic brain injury (TBI) patients with co-occurring depression, smaller hippocampal volume was associated both with development of mood dis-orders and poorer vocational outcome at 1 year, suggesting a strong link between hippocampal integrity and a( ective and cognitive function-ing (Pournaja1 -Nazarloo et al., 2007). A study of 34 inpatients with depression and 34 control subjects reported reduced hippocampal vol-ume in MDD and a relationship between lower hippocampal volume and poorer performance on the WCST, but not the AVLT (Frodl et al., 2006). Furthermore, there was no association of cognitive measures with frontal volumes. A longitudinal study of 61 depressed patients over the age of 60, compared with 40 age and


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volume (Ste( ens et al., 2003). � ese studies suggest that the frontal lobes are indeed rele-vant when understanding the pathophysiology of depression and the potential for concurrent neuropsychological decrements.

Other brain regions, and related a( ect and cognitive measures, have received very lit-tle research focus thus far in mood disorders. Studies of the basal ganglia are mixed, and there appears to be a distinction in volume between simply depressed (reduced) and bipo-lar (increased) patients with mood disorders (Anand & Shekhar, 2003; Krishnan et al., 1992). One study of the thalamus with 25 bipolar and 17 unipolar patients showed no di( erences in thalamic volume from 39 control subjects (Caetano et al., 2001). � e insula has also been implicated in mood disorders, although vari-ability in measurement boundaries likely have precluded obtaining strong reliability in mor-phologic measurements, thus reducing reports in the literature about this region (Nagai et al., 2007). Voxel-based morphometry studies, which are limited in structural speci1 city, have shown more structural abnormalities in the insula in schizophrenia, and not in mood disor-ders (Nagai et al., 2007).

Of course, the relevance of subcortical and periventricular hyperintensities in mood dis-orders are of increasing interest, particularly in elderly or middle-age onset mood disorders, and these patients are more likely to exhibit cogni-tive di� culties (Bhalla et al., 2006; King et al., 1998; O’Brien et al., 2004; Sobow et al., 2006). One large study of 48 depressed patients and 73 depressed inpatients demonstrated an increas-ing odds ratio of 5.32 for subcortical hyperin-tensities in those with depression (Co( ey et al., 1993). Another of 37 patients found that white matter hyperintensities were predictors of con-version to dementia syndromes and/or persist-ing cognitive di� culties (Hickie et al., 1997). A more recent study of 41 MDD patients and 41 control subjects reported inverse relation-ships between bilateral orbital frontal volumes and subcortical gray matter lesions severity, but not deep white matter lesion severity (Lee et al., 2003). One very large study of 2546 subjects between age 60 and 64 did not 1 nd an associa-tion between cognitive measures and hippo-campal or amygdalar volume, APOE4, or white

However, notes of caution for the reader include the following: 1 rst, the frontal lobes encom-pass 33% of the cortical surface, second, there are 1 ve, possibly six, frontosubcortical circuits regulating everything from eye movements to emotional functioning, and third, the zeitgeist remains focused on frontal pathology as a cause, or result, of mood disorders. As such, one can-not conclude that the frontal lobes are uniquely involved in mood disorders. Methodological challenges in measuring speci1 c frontal regions are formidable, whether by Brodmann area or by speci1 c landmarks. � e subgenual anterior cingulate has been demonstrated to be smaller in several studies (Botteron et al., 2002; Drevets et al., 1997; Ongur et al., 1998, also Coryell et al., 2005) with one of the negative studies showing a functional abnormality in the same region (Pizzagalli et al., 2004). One other group studied both anterior and posterior cingulate volume in 21 unremitted and 10 remitted patients with unipolar depression compared to 31 control subjects, showing reduced volumes in all four (right, le< by anterior, posterior) ROIs in those with unremitted depression, but only in the le< anterior cingulate in the subset with remitted depression, both compared to the control group (Caetano et al., 2006). � e remitted and unre-mitted groups did not di( er in ROI volumes, suggesting that reduced power in the subset analysis may have masked di( erences between the patient groups, as well as between the remit-ted patient and control groups. A study of 44 elderly patients with depression showed larger lateral ventricles in those depressed patients with prior ECT compared to those without ECT (Simpson et al., 2001). Furthermore, there were multiple correlations of neuropsychologi-cal measures with frontal (reverse digit span), temporal (Trial 1 of the RAVLT, perseverative errors on the WCST, copy of the Rey–Osterrieth Complex Figure Test, and memory for the Rey–Osterrieth Complex Figure Test), and parietal (Digit Symbol Substitution Test, Trial 1 of the RAVLT, and reverse digit span) lobe volumes but not volume of the lateral ventricle. Another study compared 30 elderly depressives and 40 controls, demonstrating a negative relationship between perseverative errors and a positive rela-tionship with total correct on the Benton Visual Memory Test with le< orbital frontal cortical


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more challenging. Finally, due to current funding structures, recruitment di� culties given restric-tive exclusion criteria for imaging protocols, and the expense of imaging technology, most, if not all, imaging studies are underpowered, limiting convergence across functional imaging studies, particularly in a biologically heterogeneous set of mood disorders. � is section is subdivided into a brief review of neurotransmitter stud-ies, resting glucose/blood & ow studies, a( ective challenges, and cognitive challenges.

Before beginning to review functional imag-ing studies in mood disorders, it is valuable to review the a( ective neuroscience literature, as much of this data was the genesis for imaging paradigms described below. In fact, in future chapters on mood disorders, emotion process-ing may more appropriately be placed under the “traditional” neuropsychological pro1 le of mood disorders. Herein, we di( erentiate between emotion perception and processing as they might di( er from emotional experience. � e former denotes a cognitive process in the same vein as visual perception and process-ing, whereby, the latter refers more broadly to a gestalt of experience. Behavioral studies of emo-tion processing abnormalities in mood disorders have o< en focused on aspects of behavior such as mood-congruent memory biases (Gotlib et al., 2004, 2005; Rude et al., 2002), negative process-ing biases (Danion et al., 1991, 1995; Watkins et al., 1992, 1996; White et al., 1992), memory priming (Bradley et al., 1995; Mogg et al., 1993), and interference e( ects. � ese studies indicate a proclivity for processing and remembering negative stimuli, and better delayed memory for such stimuli, in those with mood disorders. Facial emotion stimuli have also been used to determine whether depressed individuals expe-rience di� culty with processing and classifying the emotion expressed in these faces. A major-ity of these studies indicates that depressed individuals have di� culty in correctly classify-ing emotions in facial stimuli (Bouhuys et al., 1996, 1999; Gur et al., 1992; Langenecker et al., 2005; Mikhailova et al., 1996; Nandi et al., 1982, Persad & Polivy, 1993). � ese data have formed a basis for exploring the functional underpin-nings of a( ective irregularities in mood disor-ders, primarily in fMRI (below), but also in PET and SPECT studies.

matter hyperintensities, but rather found these decrements to be associated with psychiatric symptoms, poor physical health, and personal-ity factors (Jorm et al., 2004). A subset analysis of this sample suggested that degree of white matter hyperintensities in 475 elders was related to depressive symptoms, potentially mediated by smoking and physical disability (Jorm et al., 2005). � e disruption of networks important for cognitive and a( ective processing is the likely consequence of white matter hyperintensities, even if the underlying pathology behind white matter hyperintensities remains under debate (Cummings & Benson, 1984; Lamberty & Bieliauskas, 1993).

Functional Neuroimaging and Cognitive and Affective Neuroscience Research

Tremendous progress has been achieved in functional imaging studies of mood disor-ders in the last decade. Indeed, the 1 eld has progressed to the point of trials for deep brain stimulation with subgenual cingulate, ventral striatal and le< inferior frontal targets for treat-ment resistant depression (Mayberg et al., 2005; Schlaepfer et al., 2007), as well as in use of repet-itive transcranial magnetic stimulation (rTMS) (McLoughlin et al., 2007). In addition, imag-ing data are being used to prospectively predict treatment response, highlighting both the het-erogeneity in mood disorders and the speci1 city of actual brain function to illness and treatment parameters (Brody et al., 1999; Langenecker et al., 2007b; Mayberg et al., 1997; Pizzigalli et al., 2001). Herein, we will review very brie& y functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and sin-gle photon emission computed tomography (SPECT) studies of mood disorders, again with speci1 c focus on those studies using larger sam-ple sizes and directly assessing relationships of activation with clinical and cognitive variables. We note that the time and spatial sensitivity for PET/SPECT and fMRI di( er, as well as the cogni-tive/a( ective/rest paradigms used, which might easily explain the apparent discrepancy between PET/SPECT and fMRI results. In addition, arti-fact and signal voids in orbital and medial tem-poral regions in fMRI make imaging these areas


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of MDD, prefrontal cortex, basal ganglia, and medial temporal areas are perhaps more con-sistently shown to deviate from controls using blood & ow and glucose studies with PET (Liotti et al., 2000; Milak et al., 2005; Videbech, 2000). One PET study of 40 patients with depression showed diminished rCBF-to-cognitive mea-sures relationships when compared to similar relationships in 49 healthy controls (Ravnkilde et al., 2003). One unique study used acute tryp-tophan (precursor to serotonin) depletion in eight remitted men, showing diminished ven-tral anterior cingulate and orbital frontal cortex H2(15)O PET a< er depletion associated with increased depression symptoms (Smith et al., 1999). � ose who exhibited related slowing in verbal & uency had diminished anterior cingu-late activity.

Functional imaging studies with cognitive and a( ective challenges in mood disorders have exploded, particularly within the last 5 years. In addition, increasing sophistication in mea-sure design, combined with tighter recruitment protocols and relatively larger sample sizes, has resulted in exciting new 1 ndings within the 1 eld. As before, we will highlight studies, with larger sample sizes and attempts to relate func-tional abnormalities in mood disorders with clinical and cognitive variables, with the goal of increasing con1 dence about brain regions and systems that are a( ected in mood disorders.

A( ective neuroscience studies in mood dis-orders using fMRI focused heavily on experien-tial aspects of emotion processing, for example, passive viewing and resting activation studies (Bench et al., 1993; Dolan et al., 1994; Kalin et al., 1997; Sheline et al., 2001; Whalen, 1998; Wright et al., 2001). Stimuli have included faces, com-plex visual scenes, and more recently semantic stimuli, some of which are rated as to personal relevance (Canli et al., 2004; Fossati et al., 2003; George et al., 1993, 1994, 1996; Gilboa-Schechtman et al., 2002; Gorno-Tempini et al., 2001; Iidaka et al., 2001; Kensinger & Corkin, 2004; Maddock et al., 2003; Ochsner et al., 2004; Phan et al., 2003; Siegle et al., 2002, see Figure 22.2 for an example from our own work, see also the color version in the color insert section). Physiological reactivity to emotional stimuli and emotion induction is distinctly dif-ferent in depressed compared to control groups

Imaging with transmitter-speci1 c ligands using SPECT and PET suggests decreased 5HT1-A and 2-A binding in medial tempo-ral structures in mood disorders, although the data are not consistent, with a( ects of gender, age of onset, current age, and illness severity all playing a potential role in these discrepancies (Kennedy & Zubieta, 2004). Studies with dopa-mine ligands, such as DRD2 and endogenous opiods, have received relatively less research focus in mood disorders (Kennedy et al., 2006; Larisch et al., 1997), but these studies also sug-gest relatively decreased binding in mood dis-orders. � ere is some suggestion that dopamine binding may di( er by mood disorder subtype. In one small study, those with psychomotor retardation and anhedonia associated with decreased DRD2 binding, whereas those with impulsivity showed normal binding in le< cau-date (Martinot et al., 2001). A more recent study of a mixed group of 12 suicide attempters, most with mood disorders, suggested higher impul-sivity was related to lower serotonin (5HTT) and dopamine (DAT) binding potential, with relationships most strongly located in the basal ganglia, and extending into insula for serotonin. Unfortunately, there is mixed evidence related to whether treatment of depression results in a signi1 cant change in dopamine and serotonin concentrations (Argyelan et al., 2005; Moses-Kolko et al., 2007). Important for the purposes of this chapter, these neurotransmitters are rep-resented quite densely in the same anatomical areas discussed up to this point: frontal, ante-rior and medial temporal, insular, and subcor-tical areas.

Fluorodeoxyglucose (FDG), H2(15)O, and resting cerebral blood & ow (rCBF) PET stud-ies in depression implicate a network of fron-tal and limbic areas involved, much like that reported in the section on ‘Neuroanatomy of Mood Disorders’. Subgenual anterior cingulate abnormalities in rest and a( ective paradigms have been reported, with hypometabolism in MDD and in the depressed phase of bipolar disorder (Drevets et al., 1997; Kennedy et al., 1997; Kegeles et al., 2003; Mayberg et al., 1994). � e abnormalities in subgenual anterior cin-gulate resolve with e( ective treatment (Brody et al., 2001; Kennedy et al., 2007). � e same brain regions implicated in anatomical studies


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has been termed the default network (includ-ing medial, rostral, and posterior cingulate) and more task-oriented structures in depression (lateral prefrontal and parietal, Anand et al., 2005; Greicius et al., 2007), although the spec-i1 city and clinical meaning of these 1 ndings remain to be seen.

In summary, morphological and functional imaging are strongly suggesting that limbic, frontal, and subcortical regions are involved in mood disorders (see Figure 22.1), converg-ing nicely with the traditional neuropsycholog-ical pro1 le described earlier in the chapter. At present, the nature of the relationship is largely correlational—it is unclear if these abnormal-ities precede onset of the mood disorder and may place individuals at higher risk for devel-opment of a mood disorder, or if they are the result of neurobiological changes concurrent with, or resulting from, experience of the mood disorder. Nonetheless, imaging studies of fron-tal, medial temporal, and subcortical regions will likely continue in mood disorders. � ere is currently debate about relative increases and decreases in lateral activation bias (right over le< ), or cortical/subcortical (under activation of cortical, overactivation of subcortical/lim-bic), but the body of research has not yet con-verged on a consistently replicable pattern of 1 ndings (Davidson et al., 2002; Northo( et al., 2000; Phillips et al., 2003). We strongly urge researchers to more routinely include analysis of structure/function to a( ective/cognitive rela-tionships, particularly with neuropsychological variables. � ese can provide better grounding of the signi1 cance of any di( erences between those with mood disorders and control groups.

Critical Mediating Factors in Cognitive Dysfunction related to Depression

Age of Onset

� ere does not appear to be a gold standard for determining age of onset for depression due to di� culties with individual patient memories, inconsistency of report from one query to the next, and variability in depressive symptoms (Knauper et al., 1999). Authors have reported that early-onset major depression has a mean age

(Kalin et al., 1997; Ketter et al., 1996; Kumari et al., 2003; Paradiso et al., 1999; Phillips et al., 2001; � omas et al., 2001). � is disruption in neurophysiological reactions to emotion stimuli is reversible with successful treatment (Brody et al., 2001; Davidson et al., 1999; Davidson et al., 2003; Fu et al., 2004; Kalin et al., 1997; Sheline et al., 2001; see example of our own work in Figure 22.2 below). Although compel-ling, these studies have just begun to explore the nature of emotion processing di� culties in depression.

Cognitive neuroscience studies of mood dis-orders in fMRI are perhaps the most intriguing in recent years, with several recent studies indi-cating increased frontal activation in mood dis-orders relative to comparison groups. One study of interference resolution reported increased activation for MDD patients (e.g., le< DLPFC, AC) compared to the control group (Wagner et al., 2006). Studies utilizing working memory and verbal & uency tasks have generally reported more prominent activation in the control groups in frontal, basal ganglia, and parietal areas compared to MDD patients (Audenaert et al., 2002; Barch et al., 2003; Elliott et al., 1997; Holmes et al., 2005; Hugdahl et al., 2003, 2004; Matsuo et al., 2002; Okada et al., 2003), while three other studies (examining working memory, attention, and interference control, respectively) have noted greater activation in frontal areas for the MDD groups compared to the control groups in the context of preserved behavioral performance (Harvey et al., 2005; Holmes et al., 2005; Wagner et al., 2006). In this same vein, our group has used a parametric Go/No-Go test in 20 patients with MDD com-pared to 22 control subjects (Langenecker et al., 2007b). � e MDD patients exhibited decreased attention and increased inhibitory control accu-racy performance in a signi1 cant interaction, relative to the control group. � e MDD group also exhibited greater subgenual cingulate and bilateral ventral frontal activation compared to the control group during correct rejections of No-Go stimuli (inhibition).

More recently, resting functional connectivity and ROI-linked functional connectivity studies are now on the forefront of studies of mood dis-orders. � ere appears to be a disruption in the core, low-frequency “cross-talk” between what


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abnormalities. � is 1 nding was subsequently con1 rmed in a large longitudinal (Paterniti et al., 2002) and neuropathologic study (Sweet et al., 2004). Similarly, depression in old age is associated with generalized atherosclerosis (Vinkerset al., 2005) and with increased car-diovascular and noncardiovascular mortality (Vinkers et al., 2004).

In sum, while primary depression, generally 1 rst occurring in young adulthood, is asso-ciated with cognitive dysfunction related to attention, learning, and recall, the initial occur-rence of depressive symptoms in the elderly is very likely an indicator of an underlying degen-erative or cerebrovascular neurological pro-cess. For each depressive symptom observed in the elderly, the rate of cognitive decline has been observed to increase by about 5% pre-dicting cognitive decline in old age (Wilson et al., 2004), though depressive symptoms are not necessarily increased during the prodro-mal phase of Alzheimer’s disease itself (Wilson et al., 2008). From a quality-of-life perspective, late-life depression is treatable by conventional pharmacotherapies, though careful dosing must be observed (Sadavoy, 2004). � e positive change in cognitive e� ciency that is observed when primary depression is treated, however, is not likely to be seen in the treatment of late-life-onset depressive symptoms (Bieliauskas, 1993).

Severity

It is currently unclear whether severity, as de1 ned by clinician rating or self-report obtained, has any relation to the extent of neuropsychologi-cal decrements. � ere is a suggestion that being seen on an inpatient psychiatry ward is an index of disease severity that would portend greater cognitive di� culties (Burt et al., 1995), as would recurrence of depressive episodes. � is section will review studies prototypic of those examin-ing severity according to objective psychometric measures and/or location of treatment service/recruitment. We acknowledge that depression severity gradations assume a dimensional dis-tribution of cognitive decrements, related in a linear fashion. A categorical perspective, such as that used between treatment responders and treatment nonresponders, also provides an index of severity and is reviewed below in a later

of 13.7 (SD = 5.0) and late-onset depression has a mean age of 33.5 (SD = 9.5) (Klein et al., 1999). Early-onset depression is considered more malig-nant, persistent, and resistant to treatment.

� e cognitive changes associated with this adult onset depression are felt to include decreased attention on tasks that require increased e( ort, decreased initial acquisition of stimuli, and decreased retrieval of infor-mation that is encoded (Caine, 1986). Caine (1981) describes these changes as being simi-lar to a subcortical dementia. Interestingly, in their description of the syndrome of subcortical dementia, Cummings and Benson (1994) note depression to be a prominent feature. � e cog-nitive changes, however, are coincident with the severity of the depression and many are lessened if the severity of depressive symptoms is thera-peutically ameliorated (Bieliauskas, 1993).

When symptoms of depression occur for the 1 rst time in older adults, they are more likely to indicate underlying neurological change than to be manifestations of primary depres-sion (Bieliauskas, 1993). In their review of the literature, Lamberty and Bieliauskas (1993) report that most late-onset depressive symp-toms are accompanied by abnormal 1 ndings when neuroimaging is employed. In 1992, the NIH Consensus Conference on Diagnosis and Treatment of Depression in Late Life stated:

� ere is some evidence to suggest that late-onset depression is associated with a lower frequency of family history of depression but a higher frequency of cognitive impairment, cerebral atrophy, deep white matter changes, recurrences, medical comor-bidity, and mortality. (NIH Consensus Development Panel on Depression in Late Life, 1992, p. 1019)

Multiple later studies have con1 rmed the same. Kumar et al. (2000) report that atrophy and high-intensity lesions represent independent pathways to late-life depression, with patients with major depression having larger whole brain lesion volumes than controls and smaller frontal lobe volumes.

As a corollary, depression in the elderly also appears to be predictive of subsequent cognitive decline. Nussbaum et al. (1995) report that 23% of a sample of depressed patients showed cogni-tive decline over a 25-month period, along with increased white matter MRI, CAT, and EEG

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in signi1 cant cognitive decrements and under-lying neurological dysfunction (as a chronolog-ical sequelae of depression).

A history of inpatient hospitalization and/or the number of previous depressive episodes appear to be associated with the executive and memory decrements in some studies, both dif-ferent indices of clinical severity. Purcell et al. (1997) examined 20 depressed patients (19 out-patients and 1 inpatient; 12 medicated) and 20 age and education matched normal controls on the CANTAB and found attentional set-shi< ing decrements in the depressed group. On closer examination, the researchers found that a his-tory of inpatient hospitalization was associated with poorer performance on the set-shi< ing task. Paelecke-Habermann et al. (2005) stud-ied 40 euthymic patients with a history of MDD diagnosis (20 individuals with one to two epi-sodes and 20 individuals with three or more episodes) and 20 healthy controls on tasks of executive function (Behavioral Assessment of the Dysexecutive Syndrome, word & uency, and backward visuospatial span) and found worse performance in the MDD group. Furthermore, the severe MDD group performed worse than the mild group, suggesting that individuals with recurrent episodes demonstrate greater decre-ments than those with one or two episodes.

Studies of the impact of severity of illness as they relate to signi1 cance of cognitive dec-rements are limited in many respects due to nonuniformity in neuropsychological measures employed, clinical measures of severity utilized, and the broader issue of heterogeneity in mood disorders by subtype. Future studies will likely have to further subdivide into di( erent cogni-tive domains, di( erent depression subtypes, with unique mediating factors.

Effort

E( ort, motivation, and abulia continue to be key concepts in understanding mood disor-ders and related cognitive decrements (King & Caine, 1996). Indeed, the e( ort-automatic hypothesis of depression had direct roots in beliefs about how depression might a( ect moti-vation and e( ort. For example, early studies indicated that memory tasks that were more automatic (recognition) tended to be performed

section. Further, there are some mood disorders (e.g., bipolar disorder) that are o< en viewed as greater in severity relative to others (MDD). � ese sorts of subtype gradations, such as com-paring MDD to Bipolar disorder or comparing melancholic and nonmelancholic depression, are also conducted in a separate section below.

One large study of elderly patients with minor (N = 32) and major depression (N = 63) indicated poorer performance compared to a matched control group of 71 participants in initial learning on trial 1 of the CVLT and on List B, as well as in number of correct cat-egories for WCST (minor depression only), and Letter–Number Sequencing (major only (Elderkin-� ompson et al., 2007)). � ere were no signi1 cant e( ects of severity using this classi1 -cation. A very large study of 385 elderly patients showed an increase in disruption on the Stroop test between mild and moderate depression, but not with severe depression (Baune et al., 2006). A smaller study of 26 severely depressed patients demonstrated a signi1 cant positive relationship between severity of depression and measures of simple and complex reaction time, or process-ing speed (Egeland et al., 2005) and one mea-sure of memory, with a surprising number of signi1 cant negative correlations between 8 a.m. cortisol levels and memory and executive func-tioning measures.

As is probably well known, those with severe depression have poorer long-term treat-ment prognosis (Elkin et al., 1995; Sagha1 et al., 2007), which may in fact be mediated by extent of neuropsychological decrements in performance. � ere have been attempts to use chronicity as a measure of severity, includ-ing structured interviews to code number of days ill. However, it should be noted that recall bias may signi1 cantly a( ect patient reports of length of illness and number of episodes (Riso et al., 2002). One interesting study used retro-spective recall of days of untreated depression to predict hippocampal volume loss in women with recurrent depression (Sheline et al., 2003). It is important to conduct longitudinal studies to determine whether severity of symptoms is related to cognitive performance in a meaning-ful way (e.g., re& ective of permanent underlying disruption of neural networks as an etiological risk factor), or whether depression itself results


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recently found that depression, as measured by DSM-IV criteria and Geriatric Depression Scale (GDS; Yesavage), was not related to very basic measures of e( ort such as the Rey 15-Item Test (Lee et al., 1992; Rey, 1964) or the Kaufman Hand Movements Test (Bowen & Littell, 1997; Kaufman & Kaufman, 1983) in a population of elderly medical inpatients (Vadnal, 2005).

� ese di( erences in failed versus passed e( ort tests in “depressed” samples highlight the importance of using formal or informal, but validated, e( ort measures when assessing those with mood disorders, especially when the pos-sibility of primary and secondary gain becomes crucially important in interpreting the relevance of poor test performance. When no primary or secondary gain can be found, the clinician can correlate self-reported energy levels, depres-sion severity, psychomotor retardation, and so on with observed ine� ciencies in performance (everyday functioning), but estimates of opti-mal functioning levels or inference of underly-ing neuronal dysfunction in these contexts need to be carefully interpreted.

Subtypes of Mood Disorders

� ere are a number of di( erent subtypes of depression, wherein cognitive function may be greater (e.g., cerebrovascular, bipolar disorder). Furthermore, there are certain subtype-by-cognitive function distinctions, most nota-bly the prevalence of psychomotor retardation in melancholic but not in atypical depression. A rapidly emerging set of data is in cognitive decrements with co-occurring depression and medical conditions. Cushing’s disease is a rare disease with high prevalence of depression, thought to be mediated by hypercortisolism and epitomizing the glucocorticoid cascade hypothesis of depression. Review of these select subtypes exempli1 es the variability of cognitive dysfunction in mood disorders.

Cerebrovascular

� e increased prevalence of depression among patients with cerebrovascular accidents (CVAs) has long been established, with between 50% and 68% manifesting symptoms of depres-sion (Eastwood et al., 1989). � ese estimates

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just 1 ne by depressed patients, whereas more e( ortful, di� cult tasks (recall) tended to be tasks that were more di� cult for those with mood disorders (Cohen et al., 1982; Roy-Byrne et al., 1986; Weingartner et al., 1981). As noted in chapter 9 by King and Caine (1996) in the previous edition of this book, there are other theories accounting for cognitive decrements in mood disorders, including decreased resource availability, increased distraction through inef-1 cient inhibitory process, and mood congruent memory biases, though we will not go into these in detail herein.

More recently, there has been a strong and healthy debate about the genesis of cognitive decrements in patients with depression, largely revolving around the old concepts of “func-tional” and “organic” brain-based cognitive di� culties, particularly within legal contexts (Rohling et al., 2002). In a recent study of pri-marily legal cases, over 40% of subjects recruited were excluded because of documented poor e( ort using standardized measures (Rohling et al., 2002). Interestingly, the excluded group had signi1 cantly higher depression scores on the Hamilton Depression Rating Scale (HDRS) compared to the group that was not excluded, and performed signi1 cantly worse on multi-ple cognitive measures including learning and memory. A smaller percentage (15%, 8%) of patients 1 ling for workers compensation with depressive symptoms failed formal e( ort mea-sures in a study of 233 patients (Sumanti et al., 2006). Because anergia and amotivation are key symptoms of depression and these symp-toms can adversely a( ect e( ort, it is reason-able to assume that for a minority of depressed patients, “amotivation” may result in a failed e( ort test. One possible interpretation of this 1 nding is that signi1 cant primary depression may not result in permanent brain dysfunction, but rather that insu� cient engagement of moti-vational drive (evident in depression) can mimic cognitive decrements on challenging tasks. However, two recent studies have not shown any di� culty in depressed patients or those with anxiety, in passing formal e( ort measures, suggesting that depression symptoms may have very di( erent etiologies and purposes in legal as opposed to clinical settings (Ashendorf et al., 2004; Egeland et al., 2005). We have also


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past, depression associated with a medical ill-ness was considered to be a psychological reac-tion to having a medical illness and thought to have a less severe course (Boland et al., 2006). However, it is now clear that depression in the medically ill may be more di� cult to treat com-pared to depression in individuals without a comorbid medical condition (Boland et al., 2006). � ese individuals generally report more medical symptoms even when the severity of the medical disorder is taken into account (for a review, see Katon et al., 2007). Depression can be detrimental in the medically ill in that these individuals are three times less likely to adhere to medical treatment recommendations com-pared to nondepressed medically ill individu-als (Dimatteo et al., 2000). In addition, when the depression is not properly treated, it could negatively impact the morbidity and mortality of the medical illness, especially in older adults (Boland, 2006; Covinsky et al., 1999; Frasure-Smith et al., 1993; Ganzini et al., 1997; Katon, 2003; Lesperance & Frasure-Smith, 1996). For example, Frasure-Smith et al. followed 222 patients who were hospitalized for myocardial infarction for 6 months and found that MDD was an independent risk factor for mortal-ity. Overall, MDD can be identi1 ed in 7–17% of patients with chronic medical conditions (Egede, 2007). In contrast, when depression is treated, medical symptoms decrease regardless of the improvement in the medical disease sta-tus (Borson et al., 1992).

� e cognitive pro1 le of the medically ill indi-viduals with depression should not di( er from the traditional pro1 le described above. However, additional physical symptoms including pain and increased fatigue, which are more com-mon in certain medical populations including cancer, chronic pain, and 1 bromyalgia need to be taken into consideration when interpreting test results. Neuropsychological investigations in the non-neurological, medically ill individu-als with depression are scarce with only a few that have focused on 1 bromyalgia and chronic fatigue syndrome (Johnson et al., 1997; Landro et al., 1997; Suhr, 2003). Nonetheless, it appears that cognitive ine� ciencies are generally associ-ated with the individual’s presenting symptoms such as fatigue or depression. For example, Suhr investigated 28 individuals with 1 bromyalgia,

rendered the occurrence of “melancholia in up to 25% of patients,” with minor or masked depression in 75%–95% of cases (Wiart, 1997). A more recent study has demonstrated that patients with ischemic stroke have double the risk of depression compared with those with-out stroke (11.2% versus 5.2%) with a greater incidence of depression corresponding to more severe CVAs, particularly in vascular territories supplying limbic structures (Desmond et al., 2003). � ough there has been speculation that the distribution of CVAs, especially as related to syndromes of aphasia, may speci1 cally relate to depressive symptoms, more recent evidence suggests that screening for aphasia across stud-ies is highly inconsistent and that conclusions as to relationships between aphasia subtypes and depression are not justi1 ed (Townend et al., 2007). Laska et al. (2007) alternatively suggest that depression diagnosis and severity can be reliably made during the acute phases of apha-sia and suggest that depression can be identi1 ed in at least 24% of such patients.

Nevertheless, there remains little doubt that depression is common following CVA. � ere has been the suggestion of a vascular depression hypothesis, that is, that cerebrovascular disease may predispose, precipitate, or perpetuate some geriatric depressive syndromes, It is hypothe-sized that prefrontal systems are disrupted with associated executive dysfunction (Alexopoulos et al., 1997), mechanisms discussed in the sec-tion on ‘Neuroanatomy of Mood Disorders’.

Secondary to Medical Condition

Depression is a common comorbid condition in medical illnesses including cancer (Chochinov, 2001; McDaniel et al., 1995; Spiegel, 2001), dia-betes (Anderson et al., 2001; Ciechanowski et al., 2003), heart disease (Ruo et al., 2003), asthma (Eisner et al., 2005), chronic obstructive pulmonary disease (Yohannes et al., 2000), obe-sity (� omsen et al., 2006), migraine headaches (Molgat & Patten, 2005), chronic fatigue syn-drome (Patten et al., 2005), 1 bromyalgia (Ahles et al., 1991; Patten et al., 2005), and chronic pain (McWilliams et al., 2003). A bidirectional rela-tion between depression and medical illnesses appears to exist, with each possibly altering the normal course of illness of the other. In the


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hippocampus, whereas improvements in mood are associated with increases in the caudate (Starkman et al., 1999, 2003, 2007). Cushing’s disease provides a model for how excessive stress hormones can have potentially reversible cognitive and a( ective sequelae, highlighting the deleterious a( ects of stress hormones, the link between brain morphometry and cogni-tive and a( ective symptoms, and the potential for plasticity in brain and neuropsychologi-cal functioning in mood disorders (McEwen, 2002). Although focus has been primarily on amygdala, hippocampus, and caudate thus far, future studies by our group are targeted toward e( ects of hypercortisolism on the entire medial frontal–subcortical circuit.

Studies of hypercortisolemia in other mood disorders have also helped build the connec-tion between cortisol/ACTH levels and cogni-tive dysfunction. � is may be in part due to the heterogeneity in depressive samples and relative variability of neuroendocrine measurements across subjects. Hypercortisolemia occurs in approximately 50% of individuals with depres-sion, and treatment with agents to reduce cortisol levels can result in improved cognitive perfor-mance (Golomb et al., 1993; Young et al., 1994, 2001, 2004). One study of 23 severely depressed patients found signi1 cant negative correlations between executive and memory functions and 8 a.m. cortisol levels (Egeland et al., 2005). In a study of 102 female outpatients with remit-ted MDD, those with recurrent depression had higher cortisol levels than the nonrecurrent women (Bos et al., 2005). Elevated cortisol but not ACTH levels have also been reported in a study of 29 participants with psychotic major depression compared to 26 healthy controls and to 24 participants with nonpsychotic major depression (Gomez et al., 2006). Some modest correlations were reported between measures of executive functioning, processing speed, and memory with cortisol levels. Furthermore, in a study of 17 patients with bipolar disorder, euthymic phase, and 16 matched control sub-jects, post-dexamethasone cortisol was pos-itively correlated with number of errors on a working memory task (Watson et al., 2006). It is unclear whether hypercortisolemia mediates cognitive decrements in mood disorders, and the relationships between HPA axis measures

27 individuals with chronic pain, and 21 healthy controls on measures of intellect, memory, exec-utive functioning, attention, and psychomotor speed, as well as on self-reported measures of depression, pain, fatigue, and cognitive com-plaints. � e 1 bromyalgia group reported more memory complaints, fatigue, pain, and depres-sion than the other groups. Although no dif-ference in performance was found on cognitive measures when fatigue, pain, and depression were taken into account, the severity of depres-sion was associated with memory performance and fatigue was associated with psychomotor speed.

Cushing’s Disease and Hypercortisolemic Mood Disorders

One very well-understood model for describ-ing the neurobiological sequelae of mood dis-orders is the glucocorticoid cascade hypothesis (Sapolsky, 2000). In this model, the increased stress o< en observed in mood disorders results in excessive release of CRF, signaling release of ACTH, resulting in subsequent release of cortisol. In 33% of individuals with MDD, an abnormal ACTH response to the steroid dexamethasone is indicative of failed feed-back mechanism, putatively a result of chronic excessive glucocorticoids (Sonino & Fava, 1996; Young et al., 1991). Cushing’s disease is a rare disorder o< en thought to typify the glucocorti-coid model, as there is sustained, elevated cor-tisol levels for as long as several years before the condition is diagnosed and treated (1961). � e excess cortisol levels have been associated with a 60% prevalence of mood irregularities, typically depression. Further, treatment of Cushing’s disease typically results in alleviation of depressive symptoms (Sonino et al., 1993; Starkman et al., 1981, 1986). � e remission of cognitive di� culties thought to be secondary to Cushing’s disease is more equivocal in nature, with some studies showing no improvement, and others showing improvement in memory and executive functioning (Dorn & Cerrone, 2000; Dorn et al., 1997; Forget et al., 2002; Heald et al., 2004; Hook et al., 2007; Mauri et al., 1993; Starkman et al., 2001). One study reported that improvements in memory are positively associ-ated with posttreatment volume increases in the


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memory, executive function, and psychomotor speed (Ferrier et al., 1999; Martinez-Aran et al., 2004; Rubinsztein et al., 2000; Zubieta et al., 2001). Research on individuals with BD o< en indicates a similar pattern of cognitive ine� -ciencies that is found in individuals with MDD, with more decrements in general (Borkowska & Rybakowski, 2001; for reviews see Olley et al., 2005; Quraishi & Frangou, 2002). For example, a study examining unmedicated individuals with MDD or BD during acute depressive episodes indicated worse performance in the BD group on the Performance IQ portion of the WAIS-R and on tests of executive function (Borkowska & Rybakowski, 2001). Speci1 cally, the BD group performed worse on the Stroop test (word read-ing and color–word interference parts), the letter & uency test, and the WCST (more perse-verative errors and fewer completed categories). However, contradicting evidence also exists, indicating no di( erence between MDD and BD patients, at least in depressed states. Fossati et al. (2004) investigated memory performance on a verbal learning task in patients with a 1 rst depressive episode, MDD, and BD in depressed states. � e researchers found poorer 1 rst trial free recall in both MDD and BD groups but not in the 1 rst-episode group and concluded that the repetition of depressive episodes a( ect ver-bal memory performance in acute depressive phases regardless of the subtype of depression. Similarly, Bearden et al. (2006) did not 1 nd any di( erence in verbal memory performance between the MDD and BD groups although both groups performed worse than the normal control group. � e pattern of cognitive decre-ments in BD patients has also been compared to that of patients with schizophrenia and found to have similarities, although in less severity (Schretlen et al., 2007).

� e clinical state or phase of illness in BD appears to a( ect the neuropsychological func-tion with mixed/manic states showing the great-est decrements and euthymic states displaying the least. Sweeney et al. (2000) examined 35 BD patients (14 in mixed or manic state and 21 in depressed state), 59 MDD patients, and 51 healthy controls on the CANTAB. Decrements in executive function, episodic memory, and spatial span were demonstrated in BD patients in mixed/manic states but only episodic

and cognition are not always evident (Caine et al., 1984). As noted before, underlying neu-ral networks that control stress responses and assist in certain aspects of cognition are nega-tively a( ected in mood disorders, but variabil-ity in presence and degree of disruption, when combined with small sample sizes and variabil-ity in clinical characterization and classi1 ca-tion, might explain inconsistencies within the literature.

Melancholic Depression

Apart from the studies of psychotic major depression in the context of hypercortisolemia, there are also a subset of studies comparing melancholic and nonmelancholic depression (Cornell et al., 1984). One study demonstrated di� culty in digit symbol and perseverative errors on the WCST on a group of speci1 cally de1 ned patients with melancholic depression, and not on the nonmelancholic group (Austin et al., 1999). A more recent study comparing 11 melancholic with 11 nonmelancholic depressed patients matched on HDRS symptoms, age, age of onset, and education reported poorer perfor-mance in intra/extradimensional set-shi< ing from the CANTAB battery, but not in paired associates learning or stockings of Cambridge (Michopoulos et al., 2006). A smaller study of seven melancholic, eight nonmelancholic, and nine control participants demonstrated slow-ing on several attention, executive function-ing, and processing speed tasks in the patients with melancholic depression but not in the nonmelancholic group (Rogers et al., 2004). It has been hypothesized that frontal dysfunction is greater in melancholic depression (Austin et al., 1999), but no imaging studies to date have tested this hypothesis. Recent neuroimaging studies focusing on anhedonia more speci1 -cally have reported decreased ventral striatal/nucleus accumbens response to positive stim-uli in depression (Epstein et al., 2006; Keedwell et al., 2005; Knutson et al., 2008).

Bipolar

Individuals with bipolar disorder (BD) o< en demonstrate worse performance than healthy individuals on measures of learning and


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in euthymic states, 33 individuals with bipolar II in euthymic states, and 35 healthy controls. � e two BD groups showed worse performance on tasks of attention and working memory, execu-tive function, and verbal memory compared to the control group, but the type II group was less impaired on tasks of verbal memory and exec-utive function. Future studies need to carefully consider subtype of bipolar disorder, as well as a consideration of mediating factors such as phase of illness and history of substance abuse and suicide attempts.

Medication Effects

� e understanding of medication e( ects in mood disorders poses several challenges in the assessment and interpretation of cognitive and a( ective data above and beyond the e( ects of the mood disorder itself. As there is consid-erable variability on cognitive functioning in mood disorders, and considerable severity of illness in these disorders, understanding any a( ects of medication becomes quite complex. For example, those with more severe illness may be more likely to receive medications that will a( ect cognitive functioning, and perhaps even higher doses of these medications than those with less severe illness. As medications are not randomly assigned to patients, there is a poten-tial for medication by severity interactions that may confound interpretation of either one of these alone. As such, the clinician should bal-ance knowledge of the potential cognitive side e( ects of medication with those that could be a result of the disorder itself. In some cases, it may be advisable to use medication washouts to bet-ter determine medication as opposed to illness e( ects on cognition, while also being aware of greater potential for relapse and/or complica-tions in such instances.

Anticholinergics

Medications with anticholinergic e( ects are fre-quently prescribed for depression, particularly tricyclic antidepressants, though this class also includes selective serotonin reuptake inhibitors (SSRIs) such as Paroxetine and atypical anti-depressants such as Venlafaxine. Caution is urged about use of drugs with anticholinergic

memory decrement was shown in BD patients in depressed states and patients with MDD. A recent meta-analysis of BD individuals in euthymic states (Robinson et al., 2006) revealed greater decrements on measures of executive function and memory compared to measures of attention and processing speed. Speci1 cally, large e( ect sizes were found for category & u-ency, backward digit span, and total learning score on the RAVLT and the CVLT; medium e( ect sizes were found for Stroop Color–Word Inference Test, Trail-Making Test B, WCST (perseverative errors and categories), and short- and long-delay free recall of the memory tests; and a small e( ect size was found for letter & u-ency and forward digit span. In a recent review, Robinson and Ferrier (2006) indicated that fac-tors in& uencing the severity of neuropsycholog-ical ine� ciencies in euthymic bipolar patients include the number of previous manic episodes, hospitalizations, and length of illness. A consis-tent 1 nding was the negative relation between the number of manic episodes and performance on tasks of verbal memory and executive func-tion. Speci1 cally, regarding memory function, although encoding is more commonly impaired than retention in euthymic BD individuals, the increasing number of manic episodes was asso-ciated with poorer retention. A recent study by Malhi et al. (2007) followed 25 patients with BD over 30 months and assessed them in hypo-manic, depressed, and euthymic phases of ill-ness. Decrements in executive functioning, memory, and attention were seen in both hypo-manic and depressed states with additional 1 ne motor de1 cit in the depressed state. In the euthy-mic phase, mild attention and memory decre-ments were found. Taken together, although euthymic BD patients show relatively less cog-nitive ine� ciency, persisting decrements, albeit mild, appear to be in components of executive function and memory.

It is worth mentioning that the type of BD also has an e( ect on cognitive function with BD type I being associated with worse cog-nitive outcome than compared to BD type II. Relatively few studies have speci1 ed the type of BD and a fewer studies have examined the di( er-ences between the two types (Malhi et al., 2007; Zubieta et al., 2001). However, recently Torrent et al. (2006) examined 38 individuals with BD I


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2004). A comprehensive review of benzodiaz-epine e( ects is available, with expected e( ects of sedation and cognitive decrements in atten-tion, executive functioning, and memory to be expected (Bu( ett-Jerrott & Stewart, 2002). � e e( ects of benzodiazepines, by way of increasing sleepiness, have been linked to thalamic glucose metabolism in nine healthy control subjects (Volkow et al., 1995). Further, tolerance to detri-mental memory e( ects of benzodiazepines does not appear to occur with long-term use whereas some tolerance to psychomotor e( ects of diaz-epam was present (Gorenstein et al., 1994). � is was demonstrated using 10-mg diazepam administration on memory in 28 long-term diazepam users with anxiety. � us, benzodiaz-epines can a( ect attention, executive function-ing, and memory, while increasing sedation, perhaps further e( ecting cognitive functioning in those with mood disorders.

Opiates

Given the high rate of mood disorders in indi-viduals with chronic pain or cancer, the evalua-tion of opioid medications, which are commonly used in these medical populations, on cognitive functioning is necessary (Haythornthwaite et al., 1998; Turk & Brody, 1992). � e concern for possible adverse e( ects of opioid use has been associated with the presence of opiate recep-tors in the neural structures that are involved in attention, learning, and memory (Payne, 1990). � e complexities of research in medical popula-tions using opioids including varying types of opioid medication and doses, interactions with other medications, and participant variables make drawing any 1 rm conclusions regard-ing the e( ects of opioids on cognition di� cult. However, there is evidence that short-term use of opioid medication in healthy individuals can a( ect psychomotor performance on tasks such as Digit Symbol Substitution Test, reaction time, and 1 nger tapping (Kerr et al., 1991; Zacny et al., 1994). Some studies suggest that when individu-als with chronic pain and healthy individuals are compared on cognitive tasks a< er taking opioids, the drug has a less deleterious e( ect on cognition in individuals with chronic pain, likely due to pain relief and/or the pain possibly counteracting the possible e( ects of opioids in

properties, especially in the elderly, due to potentially exaggerated side e( ects such as confusion, and memory and concentration decrements (Sadavoy, 2004). Use of SSRIs may result in subjective memory di� culties, at least in those with signi1 cant psychopathology (Wadsworth et al., 2005). Particular attention to dosing in older patients is recommended, with some ranges based on ¼–½ of the general adult dose. We recently found that when careful dos-ing with drugs with anticholinergic properties in elderly inpatients is followed, signi1 cant cog-nitive side e( ects do not appear to be of major concern (Harik et al., 2008). Indeed, by and large successful treatment may in fact increase memory, psychomotor, and attentional func-tioning (Brooks & Hoblyn, 2007).

Benzodiazepines

Patients with mood disorders, perhaps more o< en those with signi1 cant anxiety or insom-nia, are frequently prescribed a benzodiazepine in addition to a primary mood medication. � e impact of benzodiazepines on cognitive func-tioning is fairly well understood and is one of the reasons why they are typically used at night or as needed. For example, on-drug driving performance on a simulator in a double-blind, crossover study of 18 healthy volunteers was signi1 cantly poorer than nondrug performance with both extended and immediate release alprazolam (LeuW ens et al., 2007). � ere were also e( ects of immediate release alprazolam on divided attention reaction time at 1-, 2.5-, and 5-hours postadministration, for Stop RT on a stop signal task at the same intervals, and for delayed recall on a word-learning test at 1 hour. E( ects for extended release were present on cognitive tests at 1- and 2.5-hour intervals compared to placebo. Lorazepam, and not chlorpromazine, administration to 72 healthy adults resulted in impaired free recall and word-completion in an earlier study (Danion et al., 1992). Furthermore, in a comparison of 328, 65–80 year olds, 57 of whom were chronic benzodiazepine users, chronic benzodiaze-pine use resulted in higher rates of postoper-ative confusion (26% compared to 13%), even though measures of anxiety and depression did not di( er between the two groups (Kudoh et al.,


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treatment response, followed up by studies to initiate alternative treatment strategies for those with poor prognosis for positive response to tra-ditional treatments. Screening instruments for use in psychiatry and family medicine clinics are available and can be readily integrated into clinical care settings (Gualtieri et al., 2006; Gur et al., 2001a, 2001b; Langenecker et al., 2007a).

A signi1 cant limitation of this section is that most studies of treatment response predictors are much too small in sample size to rule out pre-dictors as being irrelevant in estimating poten-tial for treatment response. � is would suggest, however, that any convergence across these small studies is worthy of further and more detailed consideration and study. A study of 53 inpatients with depression who were treated with com-bined pharmacological and psychotherapeutic treatments used cognitive styles (e.g., dysfunc-tional attitude and extreme thinking) to predict treatment response at 6 months, noting that less changes in both measures predicted a more rapid return of depressive symptoms (Beevers et al., 2007). In a relatively larger study of 55 depressed patients, better verbal & uency, and poorer Rey–Osterrieth Complex Figure Test, Benton Visual Retention Test, WAIS-R arith-metic, Block Design, and Similarities, as well as poorer Hooper Visual Organization Test per-formance, were signi1 cant predictors of depres-sion remission in HDRS symptoms to SSRIs at 6 weeks of treatment (Kampf-Sherf et al., 2004). Another study of open label SSRI treatment in 35 patients with MDD demonstrated that bet-ter verbal & uency performance was a signi1 -cant, positive prognostic indicator of eventual treatment response (Taylor et al., 2006); this study was supported by a smaller study of 14 middle-aged patients with depression (Dunkin et al., 2000). In the study by Dunkin et al., an omnibus measure of executive functioning including verbal & uency was poorer in the non-responders (n = 6) compared to the responders (n = 8). Another study of 45 depressed elderly patients failed to show any di( erences between responders and nonresponders on the Mattis Dementia Rating Scale (Butters et al., 2000), although this instrument may not be sensitive enough to di� culties observed in mood disor-ders. A larger study of 112 elderly patients with depression showed poorer Stroop interference

the CNS (Haythornthwaite et al., 1998; Lorenz et al., 1997; Sjogren et al., 2000; Twycross, 1994; Vainio et al., 1995). In patients who take opi-oid medications on a long-term basis, cognitive ine� ciencies are suggested to occur during the 1 rst few hours a< er a dose is given and dur-ing the 1 rst few days of use (for reviews, see Chapman et al., 2002; Zacny, 1995). A recent study (Byas-Smith et al., 2005) evaluated driv-ing ability in a predetermined route in a com-munity; performance on the Test of Variables of Attention and on the Digit Symbol Substitution Test in 21 patients with chronic pain with stable regimens of opioid analgesics, 11 patients with chronic pain without opioid use, and 50 healthy controls were evaluated. No signi1 cant di( er-ence in driving ability, sustained attention, or psychomotor speed were found, suggesting that at least some individuals with chronic pain who regularly use opioids do not display signi1 cantly diminished cognitive abilities.

Predicting Treatment Response in Mood Disorders

� ere is an emerging corpus of data suggesting that neuropsychological and cognitive/a( ec-tive neuroscience techniques may be valuable in prospectively predicting treatment response in mood disorders. � ere is, however, an uneasy and nonproductive tension between insurance companies mindful of increasing health-care costs, a relative paucity of treatment stud-ies examining alternative treatment predic-tive models in mood disorders, and a general unwillingness to integrate neuropsychological techniques into psychiatric clinics. � is is per-haps a le< -over of the remaining stigma toward mental illnesses, as opposed to the “hard” neu-rological syndromes where permanent, irre-versible, and far more severe cognitive sequelae are more frequently observed. It should not be lost on the reader that perhaps one of the great-est areas for impact and improvement in health care could be the timely and judicious appli-cation of neuropsychological evaluations in psychiatry clinics. � e current trial and error, “cheapest or newest drug 1 rst” treatment model for mood disorders can likely be improved with large clinical trials using demographic, clinical, and neuropsychological measures to predict


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structural, functional, and behavioral abnor-malities in mood disorders is extended to some extent to include real outcome variables. Future studies can look at correlation of these di( erent dependent variables as they relate to changes with treatment, in order to assess static (trait) versus phasic (state) abnormalities and decre-ments in function. � ere is great hope that these measures can be used within clinical settings to inform clinicians about prognosis for treatment response to standard pharmacotherapies, has-tening alternative treatments, and aiding in the development of novel treatments.

Conclusions

Neuropsychology, along with cognitive and a( ective neuroscience research, has taken part in a dramatic transformation in the apprecia-tion for, and understanding of, the neurobio-logical systems a( ected in mood disorders. It is increasingly accepted that mood disorders fre-quently co-occur with cognitive di� culties and further that such di� culties may be reversible in some individuals, but not in others, based on certain characteristics outlined herein (e.g., age of onset, co-occurring medical conditions). Further, genetic and biological risks for devel-oping mood disorders may co-occur with cog-nitive weaknesses that are evident long before evidence of an a( ective disorder emerges and are even present to some extent in relatives of those a( ected with mood disorders.

In the present chapter, we have demonstrated a neuroanatomical network that is a( ected in a majority of individuals with mood disor-ders, including amygdala, hippocampus, basal ganglia, thalamus, anterior temporal, insular, ventral, medial, and dorsal prefrontal areas. It should not be lost on the reader that most of these areas are all part of a ventral cingulate–medial prefrontal circuit, as described by Cummings (1993) and further subdivided by Rolls (Rolls, 1999) in lateral and medial orbital–frontal cir-cuits. � e exception is dorsolateral prefrontal cortex, which is a distinct dorsal anterior cin-gulate–dorsal prefrontal circuit that does inter-act with the corticolimbic circuit. � e cognitive and a( ective 1 ndings in mood disorders overlay nicely onto the idea of disruption of these three frontosubcortical circuits, including disruptions

and Initiation/Perseveration scores from the Dementia Rating Scale in the nonresponder group (n = 44) compared to the responders (Alexopoulos et al., 2005).

Brain imaging studies, including morpho-logic, EEG, PET, and fMRI have also dem-onstrated promise in predicting treatment response in mood disorders, most commonly depression. For example, a study comparing 20 subjects with a lengthy depressive episode (greater than 2 years) demonstrated reduced verbal memory performance and decreased le< temporal and hippocampal volume when compared to 20 remitted and 20 control sub-jects (Shah et al., 1998). In a larger PET study of 39 outpatients with MDD, decreasing ventral anterior cingulate and anterior insula activ-ity was associated with improvement in anx-iety and tension symptoms, increasing dorsal anterior cingulate activity was associated with improvement in psychomotor retardation, and increasing dorsolateral prefrontal activation was associated with improvement in cognitive symptoms, all from the HDRS and Pro1 le of Mood States (Brody et al., 2001). In our own study of 20 patients with MDD using parametric Go/No-Go task during fMRI, activation during correct rejections of No-Go stimuli at pretreat-ment in bilateral inferior frontal, right middle frontal, le< amygdala and nucleus accumbens, and subgenual anterior cingulate was highly predictive of eventual response to s-citalopram in 15 completers (Langenecker et al., 2007b). An fMRI study using an emotion processing probe also demonstrated that amygdala activation was predictive of treatment response to cogni-tive behavioral therapy (Siegle et al., 2006). One other PET and an EEG study demonstrated that subgenual and rostral anterior cingulate activity, respectively, were positive predictors of treatment response to SSRIs (Mayberg et al., 1997; Pizzagalli et al., 2004).

� e neuropsychological and brain imaging studies predicting treatment response strongly con1 rm several recurrent themes within this chapter. Disruptions in frontal and limbic acti-vation and in tasks thought to be dependent on these brain circuits are prospectively, as well as retrospectively, valuable in predicting and understanding treatment response in mood disorders. Herein, the correlation between


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Given the traditional and more recent 1 ndings in mood disorders, at a minimum we recom-mend that an assessment of depression-related cognitive functioning include the following:

Estimate of baseline functioning less 1. dependent on e( ort and memory retrieval.

Direct or validated indirect measures of 2. e( ort, regardless of the possibility of primary or secondary gain.

Memory measures that rely on repeated 3. exposure to the same stimuli such that a learn-ing curve can be established.

Measures of executive functioning that 4. tap into processing speed, short-term memory, sustained attention, and problem-solving areas.

Fine motor dexterity and speed.5. Emotion perception and processing.6. Objective measures of mood and 7.

psychopathology.

Assessments with younger adults should also be attentive to the higher incidence of mood disorders in those with learning and attention de1 cit disorders, and will likely bene1 t from judicious use of achievement tests. Evaluations with older adults may bene1 t from using remote memory measures and cued recall techniques that can help distinguish between mood and amnestic disorders (Dierckx et al., 2007). Finally, the neuropsychologist should be leery of pro1 les associated with any given psychiat-ric condition, as at best there is overlap between many di( erent psychiatric conditions, with no validated stable and unique elements for any given mood disorder.

Acknowledgments

We acknowledge the National Alliance for Research on Schizophrenia and Depression (SAL) and NIH K-12 Clinical Research Career Development Award (SAL). We thank Jon-Kar Zubieta, MD, PhD and Sara L. Wright, PhD for their review of this chapter and helpful comments.

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Neuropsychologists should appreciate that the distinction between “organic” and “func-tional” disorders is increasingly becoming min-imized as our understanding of neurobiological mechanisms of brain function improve. A chal-lenge remains in distinguishing poor e( ort, decreased goal-directed behavior, decreased e� ciency in planning, as secondary to mood disorders, from the same observed phenom-ena in legal or compensation contexts where primary and secondary gain are highly signif-icant factors. � ere will be greater expectation that neuropsychologists appreciate the impact of psychiatric and mood symptoms in cogni-tive functioning. Neuropsychologists can func-tion e( ectively as consultants in psychiatry and even in primary care clinics, wherein the goal is to understand illness burden and prognosis for treatment success, such that alternative and more e( ective treatments can be more e� ciently and rapidly applied. Finally, neuropsychologists should continue to be mindful of the relevance of, and need for, applied knowledge in clinical, legal, and research settings wherein mood dis-orders will continue to be prevalent.

Recommendations

We also wish to share with the reader the types of assessment strategies and instruments that will be most bene1 cial in di( erent settings.


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AQ 1: Please provide complete reference details for cross-reference ‘Cummings and Benson (1994)’ in the reference list.

AQ 2: Please expand CVAAQ 3: Please provide cross-reference for reference ‘Bishop et al., 2004’ in the text.AQ 4: Please provide cross-reference for reference ‘King et al., 1998’ in the text.AQ 5: Please provide cross-reference for reference ‘Persad & Polivy, 1993’ in the text.AQ 6: Please con1 rm whether the inserted year of publication as per cross-reference in the text is

correct.AQ 7: Please con1 rm whether the inserted year of publication as per cross-reference in the text is

correct.AQ 8: Please provide Figure 22–2.


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page 9, bottom right

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page 8, bottom right, King et al., 1998

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Please remove reference, it is not in the text.

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CVA = cerebrovascular accidents as noted in the chapter. Please clarify further if this is confusing.

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confirmed ;-)

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confirmed

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this should already be set up? just a reminder for typesetting?

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Marked set by slangen

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should read 1984, as per reference on page 25, upper right

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Figure 22-2 Example of Disrupted Emotion Processing and Regulation in MDD.AQ 8


neuropsychology of depression and related mood disorders · 222-igrant-chap22.indd...

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