Association of Cortical Thickness to Cognitive Functioning in Late Life Depression Melissa Hirt, MA, David Bickford, BA, Alana Kivowitz, BA, Joseph Brewer, BA, Michael Weiner, MD, Duygu Tosun, Ph.D., Susanne Mueller, MD, Alexandra Elite-Marcandonatou, LCSW, Diana Truran, BA , J. Craig Nelson, MD, & R Scott Mackin, Ph.D. (http://surfer.nmr.mgh.harvard.edu/) We gratefully acknowledge grant support for this study: NIMH K08 MH081065-01, UCSF Leon Epstein Fund Late Life Depression Program OBJECTIVE: This study was conducted to evaluate the association of cortical thickness to cognitive function among individuals with Late Life Depression (LLD). Given recent findings demonstrating that LLD is associated with reductions in cortical thickness, we hypothesized that cortical thickness would show stronger associations with cognition than white matter lesions (WML) and depression severity. BACKGROUND: Up to 15% of adults over the age of 65 suffer from LLD and the economic cost of LLD to society is tremendous. Cognitive Impairment (CI) occurs in up to 60% of individuals with LLD1 and represents one of the most debilitating and costly aspects of the disorder. CI in LLD is often characterized by deficits of information processing speed (IPS) and memory. However, the causes of CI in LLD are not yet clear and most previous studies have focused on association of white matter lesions (WML) to CI. While neurobiological factors contributing to cognitive dysfunction are not yet clear, both WML volume2 and cortical thickness3 have emerged as potential contributing factors. Assessment of Depression and Cognitive Function: Depression was evaluated by licensed psychologists using the Structured Clinical Interview for the Diagnoses of DSM-IV Disorders (SCID) and criteria from the Diagnostic and Statistical Manual, 4th edition (DSM-IV). Depression Severity was evaluated using the 24-item Hamilton Depression Rating Scale (HDRS). Cognitive functioning was assessed using age corrected scaled scores for the following measures: Learning (Hopkins Verbal Learning Test; HVLT-L), Memory (Hopkins Verbal Learning Test; Delayed total correct; HVLT-M), Executive Functioning (Trails B; TMT-B), Attention (Digit Span; DS), Verbal Fluency (Category Fluency; CF) and Information Processing Speed (Symbol Digit Modalities Test; SDMT). Eligibility: Eligibility criteria included older adults (> 65 years of age) with a diagnosis of Major Depressive Disorder (MDD), a SCID diagnosis of MDD, a score of 19 or greater on the HDRS, not having dementia (MMSE > 24), and willingness to participate in an MRI scan. Controls were selected on the basis of having no depression history or current depressive symptoms and a 15-item Geriatric Depression Scale of less than 3 (GDS < 3). MRI METHODS: Each participant had Magnetic Resonance Imaging (MRI) obtained on a 4 Tesla MRI (Bruker/Siemens). The image processing was performed using Freesurfer version 4.5 (http://surfer.nmr.mgh.harvard.edu/). The primary neuroimaging outcome variables in this study included cortical thickness measures for 7 cortical Regions of Interest (ROI) averaged across each hemisphere and the total volume of white matter lesions (WMLs). DATA ANALYTIC PROCEDURE: To compare the cognitive performance between the LLD and Control groups we performed ANOVAs. To identify cortical regions for subsequent linear regression analyses, we looked at the differences in adjusted means between controls and depressed participants. Using the identified regions we ran linear regression analyses to predict cognitive performance. Table 2: Cortical Thickness in the Frontal and Temporal Regions RESULTS: There was no statistical difference in age, education, or gender between groups. LLD participants demonstrated poorer performance relative to controls on the SDMT F(1, 51) = 7.78, p < .001, TMT-B F(1,48) p < .001, HVLT-L F(1,53) = 12.37, p < .001, and the HVLT-M F(1,53) = 15.13, p < .001 (Table 1). Frontal lobe cortical thickness was reduced in LLD (Middle Frontal; MF and Rostral Middle Frontal; RMF) and temporal lobe thickness (Entorhinal Cortex; EC) was thicker in LLD relative to controls (Table 2). WML volumes did not differ significantly between groups. Results of a linear regression including age, HDRS, MF-T, and WML indicated that depression severity was the strongest predictor of IPS (F = 4.96, p < .01). (Table 3). Similarly, depression severity was the only significant predictor of memory performance (F = 5.720, p < .01) (Table 4). Control Depressed F (group) p n Adj. Mean (Std. Err) Frontal Regions MF 21 7.4 (.1) 36 7.0 (.1) 5.23 .026 RMF 6.0 (.1) 5.7 (.1) 4.31 .043 CMF 35 5.6 (.1) 0.54 .466 LOF 6.7 (.1) 6.5 (.1) 1.66 .204 MOF 6.4 (.1) 3.35 .073 Temporal Regions EC 20 7.7 (.3) 34 9.0 (.2) 13.42 .001 PHip 7.8 (.2) 8.0 (.2) 0.69 .411 White Matter Lesion Volume WML V 2936.5 (923.4) 41 5083.6 (644.8) 3.40 .070 Participants: 42 LLD individuals and 21 healthy controls Mean Age: LLD: 71.8 years (SD = 7.05) Control: 69.3 years (SD = 6.4) Education: LLD: 15.9 years (SD = 2.5) Control: 16.8 years (SD = 2.4) Gender: LLD: 29% Males, 71% Female Control: 67% Males, 33% Female Depression LLD (HAM-D): 24.3 (SD = 3.8) Severity: Controls (GDS): 2.0 (SD = 2.6) MF = Middle Frontal, RMF = Rostral Middle Frontal, CMF = Caudal middle frontal, LOF = Lateral Orbitofrontal, MOF = Medial Orbitofrontal, EC = Entorhinal cortex, Phip = Parahippocampal, WMLV = White Matter Lesion Volume Table 3: Regression Analysis of Cognition and Cortical Thickness as Predictors of Information Processing Speed (Symbol Digit) Model Std. Error Beta t p A Age .075 -.279 -1.74 .087 HAM-D .033 -.370 -2.87 .006 WML-V .000 -.192 -1.40 .165 MF-T .819 -.067 -.385 .702 Table 1: Demographic Characteristics and Cognitive Measures A R2 = .233 HAM-D = Hamilton Depression Rating Scale, MF-T = Middle Frontal Total Thickness, WML-V = White Matter Lesion Volume Control Depressed F p n Mean (sd) SDMT 20 12.15 (3.05) 33 8.85 (2.69) 7.78 <.001 DS 18 10.61 (2.33) 32 10.53 (2.75) 2.26 .094 TMT B 17 10.35 (3.39) 8.45 (2.95) 3.08 .037 HVLT-L 13.55 (2.54) 34 8.71 (3.27) 12.37 HVLT-M 14.5 (2.80) 8.91 (3.25) 15.13 CF 10.67 (3.90) 12.03 (3.22) .95 .423 Table 4: Regression Analysis of Cognition and Cortical Thickness as Predictors of Memory (HVLT-Memory) Model Std. Error Beta t p AAge .058 -.189 -1.51 .138 HAM-D .039 -.504 -3.34 .002 WML-V .000 -.161 -1.24 .220 EC-T .351 .172 1.19 .240 SDMT = Symbol Digit, DS = WAIS Digit Span, TMT-B = Trails Making Test B, HVLT-L = Hopkins Verbal Learning Task Learning, HVLT-D = Hopkins Verbal Learning Task Delayed, CF = Category Fluency. A R2 = .278 HAM-D = Hamilton Depression Rating Scale, EC-T = Entorhinal Cortex Total Thickness, WML-V = White Matter Lesion Volume CONCLUSION: While LLD was found to be associated with cognitive dysfunction across several domains, our preliminary data suggests that cortical thickness was not associated with cognition in our sample. In contrast, depression severity was a strong contributor to cognitive function. This raises the possibility that other neurobiological factors may contribute to cognitive dysfunction in LLD. DISCUSSION: Our results suggest that LLD is associated with cognitive dysfunction across several domains (memory, learning, IPS); this replicated previous findings. Our findings also indicate that LLD is associated with reduced cortical thickness in frontal regions (CMF, RMF, MOF) which is consistent with a growing literature in this area. In contrast, in our sample cortical thickness in the entorhinal cortex was greater in depressed participants when compared to controls. This finding was unexpected and warrants further investigation. When evaluating the relative contribution of brain structure and depression severity on cognition across domains, our findings were consistent in that depression severity was significantly associated with cognitive function whereas WML and cortical thickness were not. REFERENCES: 1. Butters, M.A. (2004). Archives of General Psychiatry. 2. Alexopoulos, G.S. (2012). Journal of Affective Disorders. 3. Mackin, R.S. (2012). American Journal of Geriatric Psychiatry.