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Schizophrenia and bipolar disorder are associated with cognitive deficits that contribute significantly to disability. However, traditional in-lab cognitive assessments are time-consuming and not optimized for remote administration. Recent advancements in smartphone technology enable momentary cognitive assessments in a real-world context. This brief report reviews recent research in momentary cognitive assessments in individuals with schizophrenia and bipolar disorder through reviewing mobile platforms and cognitive assessments studied. A total of 14 experimental articles were reviewed, focusing on cognitive domains including visual working memory, processing speed, executive function, verbal fluency, verbal memory, social cognition, and typing patterns. The review highlights the feasibility of remote cognitive assessment with smartphones, and provides a layout of domains studied in this context, but illustrates a low volume of current research, the need for additional studies, and the potential for innovations like digital phenotyping.

Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain. With the advent of connectomics, the primary graphs or networks of interest are structural connectome (derived from DTI tractography) and functional connectome (derived from resting-state fMRI). However, most published connectome studies have focused on either structural or functional connectome, yet complementary information between them, when available in the same dataset, can be jointly leveraged to improve our understanding of the brain. To this end, we propose a function-constrained structural graph variational autoencoder (FCS-GVAE) capable of incorporating information from both functional and structural connectome in an unsupervised fashion. This leads to a joint low-dimensional embedding that establishes a unified spatial coordinate system for comparing across different subjects. We evaluate our approach using the publicly available OASIS-3 Alzheimer's disease (AD) dataset and show that a variational formulation is necessary to optimally encode functional brain dynamics. Further, the proposed joint embedding approach can more accurately distinguish different patient sub-populations than approaches that do not use complementary connectome information.

MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, such as brain development and disease. Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain networks derived from functional and structural MRI describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks is non-trivial. Most current studies aim to extract a fused representation of the two types of brain network by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object is suboptimal. However, mapping in the opposite direction is not feasible due to the non-negativity requirement of current graph learning techniques. Here, we propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.

OBJECTIVE: Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. MATERIALS AND METHODS: BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. RESULTS: We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. CONCLUSIONS: Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain.

Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve detection performance. A new approach is examined here for enhancing EEG classification performance by leveraging knowledge of spatial layout of EEG sensors. Performance of two classification models - model 1 that ignores the sensor layout and model 2 that factors it in - is investigated and found to achieve consistently higher detection accuracy. The analysis is based on the information content of these signals represented in two different ways: concatenation of the channels of the frequency bands and an image-like 2D representation of the EEG channel locations. Performance of these models is examined on two tasks, social anxiety disorder (SAD) detection, and emotion recognition using a dataset for emotion analysis using physiological signals (DEAP). We hypothesized that model 2 will significantly outperform model 1 and this was validated in our results as model 2 yielded $5$--$8\%$ higher accuracy in all machine learning algorithms investigated. Convolutional Neural Networks (CNN) provided the best performance far exceeding that of Support Vector Machine (SVM) and k-Nearest Neighbors (kNNs) algorithms., Comment: arXiv admin note: text overlap with arXiv:1812.02865

The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. EEG-based temporal dynamic functional connectomes are created based on 20 psychiatrically healthy participants EEG recordings during resting state and an emotion regulation task. Graph dissimilarity space embedding was applied to all the dynamic EEG connectomes. In order to visualize the learned manifold in a lower dimensional space, local neighborhood information is reconstructed via k-nearest neighbor-based nonlinear dimensionality reduction (NDR) and epsilon distance-based NDR. We showed that two neighborhood constructing approaches of NDR embed the manifold in a two-dimensional space, which we named Thought Chart. In Thought Chart, different task conditions represent distinct trajectories. Properties such as the distribution or average length in the 2-D space may serve as useful parameters to explore the underlying cognitive load and emotion processing during the complex task. In sum, this framework is a novel data-driven approach to the learning and visualization of underlying neurophysiological dynamics of complex functional brain data.

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

Connectomics is a framework that models brain structure and function interconnectivity as a network, rather than narrowly focusing on select regions-of-interest. MRI-derived connectomes can be structural, usually based on diffusion-weighted MR imaging, or functional, usually formed by examining fMRI blood-oxygen-level-dependent (BOLD) signal correlations. Recently, we developed a novel method for assessing the hierarchical modularity of functional brain networks-the probability associated community estimation (PACE). PACE uniquely permits a dual formulation, thus yielding equivalent connectome modular structure regardless of whether positive or negative edges are considered. This method was rigorously validated using the 1,000 functional connectomes project data set (F1000, RRID:SCR_005361) (1) and the Human Connectome Project (HCP, RRID:SCR_006942) (2, 3) and we reported novel sex differences in resting-state connectivity not previously reported. (4) This study further examines sex differences in regard to hierarchical modularity as a function of age and clinical correlates, with findings supporting a basal configuration framework as a more nuanced and dynamic way of conceptualizing the resting-state connectome that is modulated by both age and sex. Our results showed that differences in connectivity between men and women in the 22-25 age range were not significantly different. However, these same non-significant differences attained significance in both the 26-30 age group (p = 0.003) and the 31-35 age group (p < 0.001). At the most global level, areas of diverging sex difference include parts of the prefrontal cortex and the temporal lobe, amygdala, hippocampus, inferior parietal lobule, posterior cingulate, and precuneus. Further, we identified statistically different self-reported summary scores of inattention, hyperactivity, and anxiety problems between men and women. These self-reports additionally divergently interact with age and the basal configuration

Connectomics is a framework that models brain structure and function interconnectivity as a network, rather than narrowly focusing on select regions-of-interest. MRI-derived connectomes can be structural, usually based on diffusion-weighted MR imaging, or functional, usually formed by examining fMRI blood-oxygen-level-dependent (BOLD) signal correlations. Recently, we developed a novel method for assessing the hierarchical modularity of functional brain networks-the probability associated community estimation (PACE). PACE uniquely permits a dual formulation, thus yielding equivalent connectome modular structure regardless of whether positive or negative edges are considered. This method was rigorously validated using the 1,000 functional connectomes project data set (F1000, RRID:SCR_005361) (1) and the Human Connectome Project (HCP, RRID:SCR_006942) (2, 3) and we reported novel sex differences in resting-state connectivity not previously reported. (4) This study further examines sex differences in regard to hierarchical modularity as a function of age and clinical correlates, with findings supporting a basal configuration framework as a more nuanced and dynamic way of conceptualizing the resting-state connectome that is modulated by both age and sex. Our results showed that differences in connectivity between men and women in the 22-25 age range were not significantly different. However, these same non-significant differences attained significance in both the 26-30 age group (p = 0.003) and the 31-35 age group (p < 0.001). At the most global level, areas of diverging sex difference include parts of the prefrontal cortex and the temporal lobe, amygdala, hippocampus, inferior parietal lobule, posterior cingulate, and precuneus. Further, we identified statistically different self-reported summary scores of inattention, hyperactivity, and anxiety problems between men and women. These self-reports additionally divergently interact with age and the basal configuration

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

OBJECTIVE: Reward positivity (RewP), a neurophysiologic index of reward responsivity, is consistently reduced in participants with depression and, to a lesser extent, anxiety. It remains unknown, however, whether RewP can be altered as psychiatric symptoms change with treatment. The current study addressed this question by examining differences in RewP within patients before and after 12 weeks of treatment with a selective serotonin reuptake inhibitor (SSRI) or cognitive-behavioral therapy (CBT). We also examined the utility of RewP as a predictor of symptom change during CBT and SSRI treatment. METHODS: Participants were recruited between 2014 and 2017 and included adults with a primary DSM-5 anxiety or depressive disorder (n = 63) and healthy controls (n = 25). At baseline and 12 weeks, participants completed a monetary award task while electroencephalogram (EEG) was recorded. Between EEG sessions, patients completed CBT or SSRI treatment. RESULTS: At baseline, higher levels of depressive symptoms were associated with a more attenuated RewP. We found no significant differences between patients and healthy controls in the degree of RewP change across the 12 weeks; however, among patients, the extent of increase in RewP robustly correlated with the extent of decline in depressive (t = -2.21, P = .03) and anxiety (t = -2.57, P = .02) symptoms following CBT and SSRI treatment. Additionally, a more attenuated RewP at baseline predicted a greater reduction in depressive symptoms following treatment with SSRIs (t = -2.04, P < .05), but not after CBT. CONCLUSIONS: These findings highlight neural responsiveness to reward as both a mechanism and a predictor of depressive symptom change that may be used serve as an objective index of symptom improvement. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01903447.

Depression is a commonly occurring symptom in obsessive-compulsive disorder (OCD), and is associated with worse functional impairment, poorer quality of life, and poorer treatment response. Understanding the underlying neurochemical and connectivity-based brain mechanisms of this important symptom domain in OCD is necessary for development of novel, more globally effective treatments. To investigate biopsychological mechanisms of comorbid depression in OCD, we examined effective connectivity and neurochemical signatures in the pregenual anterior cingulate cortex (pACC), a structure known to be involved in both OCD and depression. Resting-state functional magnetic resonance imaging (fMRI) and 1H magnetic resonance spectroscopy (MRS) data were obtained from participants with OCD (n=49) and healthy individuals of equivalent age and sex (n=25). Granger causality-based effective (directed) connectivity was used to define causal networks involving the right and left pACC. The interplay between fMRI connectivity, 1H MRS and clinical data was explored by applying moderation and mediation analyses. We found that the causal influence of the right dorsal anterior midcingulate cortex (daMCC) on the right pACC was significantly lower in the OCD group and showed significant correlation with depressive symptom severity in the OCD group. Lower and moderate levels of glutamate (Glu) in the right pACC significantly moderated the interaction between right daMCC-pACC connectivity and depression severity. Our results suggest a biochemical-connectivity-psychological model of pACC dysfunction contributing to depression in OCD, particularly involving intracingulate connectivity and glutamate levels in the pACC. These findings have implications for potential molecular and network targets for treatment of this multi-faceted psychiatric condition.

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs.

We investigated whether graphomotor organization during a digitized Clock Drawing Test (dCDT) would be associated with cognitive and/or brain structural differences detected with a tractography-derived structural connectome of the brain. 72 non-demented/non-depressed adults were categorized based on whether or not they used 'anchor' digits (i.e., 12, 3, 6, 9) before any other digits while completing dCDT instructions to "draw the face of a clock with all the numbers and set the hands to 10 after 11". 'Anchorers' were compared to 'non-anchorers' across dCDT, additional cognitive measures and connectome-based metrics. In the context of grossly intact clock drawings, anchorers required fewer strokes to complete the dCDT and outperformed non-anchorers on executive functioning and learning/memory/recognition tasks. Anchorers had higher local efficiency for the left medial orbitofrontal and transver se temporal cortices as well as the right rostral anterior cingulate and superior frontal gyrus versus non-anchorers suggesting better regional integration within local networks involving these regions; select aspects of which correlated with cognition. Results also revealed that anchorers' exhibited a higher degree of modular integration among heteromodal regions of the ventral visual processing stream versus non-anchorers. Thus, an easily observable graphomotor distinction was associated with 1) better performance in specific cognitive domains, 2) higher local efficiency suggesting better regional integration, and 3) more sophisticated modular integration involving the ventral ('what') visuospatial processing stream. Taken together, these results enhance our knowledge of the brain-behavior relationships underlying unprompted graphomotor organization during dCDT.

BackgroundMany recent studies have separately investigated functional and white matter (WM) based structural connectivity, yet their relationship remains less understood. In this paper, we proposed the functional-by-structural hierarchical (FSH) mapping to integrate multimodal connectome data from resting state fMRI (rsfMRI) and the whole brain tractography-derived connectome.MethodsFSH first observes that the level of resting-state functional correlation between any two regions in general decreases as the graph distance of the corresponding structural connectivity matrix between them increases. As not all white matter tracts are actively in use (i.e., "utilized") during resting state, FSH thus models the rsfMRI correlation as an exponential decay function of the graph distance of the rsfMRI-informed structural connectivity or rsSC. rsSC is mathematically computed by multiplying entry-by-entry the tractography-derived structural connectivity matrix with a binary white matter "utilization matrix" U. U thus encodes whether any specific WM tract is being utilized during rsFMRI, and is estimated using simulated annealing. We applied this technique and investigated the hierarchical modular structure of rsSC from 7 depressed subjects and 7 age/gender matched controls.ResultsNo significant group differences were detected in the modular structures of either the resting state functional connectome or the whole brain tractography-derived connectome. By contrast, FSH results revealed significantly different patterns of association in the bilateral posterior cingulate cortex and right precuneus, with the depressed group exhibiting stronger associations among regions instrumental in self-referential operations.DiscussionThe results of this study support that enhanced sensitivity can be obtained by integrating multimodal imaging data using FSH, a novel computational technique that may increase power to detect group differences in brain connectomes.

BackgroundMany recent studies have separately investigated functional and white matter (WM) based structural connectivity, yet their relationship remains less understood. In this paper, we proposed the functional-by-structural hierarchical (FSH) mapping to integrate multimodal connectome data from resting state fMRI (rsfMRI) and the whole brain tractography-derived connectome.MethodsFSH first observes that the level of resting-state functional correlation between any two regions in general decreases as the graph distance of the corresponding structural connectivity matrix between them increases. As not all white matter tracts are actively in use (i.e., "utilized") during resting state, FSH thus models the rsfMRI correlation as an exponential decay function of the graph distance of the rsfMRI-informed structural connectivity or rsSC. rsSC is mathematically computed by multiplying entry-by-entry the tractography-derived structural connectivity matrix with a binary white matter "utilization matrix" U. U thus encodes whether any specific WM tract is being utilized during rsFMRI, and is estimated using simulated annealing. We applied this technique and investigated the hierarchical modular structure of rsSC from 7 depressed subjects and 7 age/gender matched controls.ResultsNo significant group differences were detected in the modular structures of either the resting state functional connectome or the whole brain tractography-derived connectome. By contrast, FSH results revealed significantly different patterns of association in the bilateral posterior cingulate cortex and right precuneus, with the depressed group exhibiting stronger associations among regions instrumental in self-referential operations.DiscussionThe results of this study support that enhanced sensitivity can be obtained by integrating multimodal imaging data using FSH, a novel computational technique that may increase power to detect group differences in brain connectomes.

This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects. Results supported the nonlinearity of brain connectivity data, as linear reduction techniques such as the multidimensional scaling yielded inferior lower-dimensional embeddings. To further validate our results, we demonstrated that for tractography-derived structural connectome more influential regions such as rich-club members of the brain are more centrally mapped or embedded. Further, abnormal brain connectivity can be visually understood by inspecting the altered geometry of these three-dimensional (3D) embeddings that represent the topology of the human brain, as illustrated using simulated lesion studies of both targeted and random removal. Last, in order to visualize brains intrinsic topology we have developed software that is compatible with virtual reality technologies, thus allowing researchers to collaboratively and interactively explore and manipulate brain connectome data.

This article presents a novel approach for understanding information exchange efficiency and its decay across hierarchies of modularity, from local to global, of the structural human brain connectome. Magnetic resonance imaging techniques have allowed us to study the human brain connectivity as a graph, which can then be analyzed using a graph-theoretical approach. Collectively termed brain connectomics, these sophisticated mathematical techniques have revealed that the brain connectome, like many networks, is highly modular and brain regions can thus be organized into communities or modules. Here, using tractography-informed structural connectomes from 46 normal healthy human subjects, we constructed the hierarchical modularity of the structural connectome using bifurcating dendrograms. Moving from fine to coarse (i.e., local to global) up the connectome's hierarchy, we computed the rate of decay of a new metric that hierarchically preferentially weighs the information exchange between two nodes in the same module. By computing "embeddedness"-the ratio between nodal efficiency and this decay rate, one could thus probe the relative scale-invariant information exchange efficiency of the human brain. Results suggest that regions that exhibit high embeddedness are those that comprise the limbic system, the default mode network, and the subcortical nuclei. This supports the presence of near-decomposability overall yet relative embeddedness in select areas of the brain. The areas we identified as highly embedded are varied in function but are arguably linked in the evolutionary role they play in memory, emotion and behavior.

This article presents a novel approach for understanding information exchange efficiency and its decay across hierarchies of modularity, from local to global, of the structural human brain connectome. Magnetic resonance imaging techniques have allowed us to study the human brain connectivity as a graph, which can then be analyzed using a graph-theoretical approach. Collectively termed brain connectomics, these sophisticated mathematical techniques have revealed that the brain connectome, like many networks, is highly modular and brain regions can thus be organized into communities or modules. Here, using tractography-informed structural connectomes from 46 normal healthy human subjects, we constructed the hierarchical modularity of the structural connectome using bifurcating dendrograms. Moving from fine to coarse (i.e., local to global) up the connectome's hierarchy, we computed the rate of decay of a new metric that hierarchically preferentially weighs the information exchange between two nodes in the same module. By computing "embeddedness"-the ratio between nodal efficiency and this decay rate, one could thus probe the relative scale-invariant information exchange efficiency of the human brain. Results suggest that regions that exhibit high embeddedness are those that comprise the limbic system, the default mode network, and the subcortical nuclei. This supports the presence of near-decomposability overall yet relative embeddedness in select areas of the brain. The areas we identified as highly embedded are varied in function but are arguably linked in the evolutionary role they play in memory, emotion and behavior.

Psychomotor slowing has been documented in depression. The digital Clock Drawing Test (dCDT) provides: (i) a novel technique to assess both cognitive and motor aspects of psychomotor speed within the same task and (ii) the potential to uncover subtleties of behavior not previously detected with non-digitized modes of data collection. Using digitized pen technology in 106 participants grouped by Age (younger/older) and Affect (euthymic/unmedicated depressed), we recorded cognitive and motor output by capturing how the clock is drawn rather than focusing on the final product. We divided time to completion (TTC) for Command and Copy conditions of the dCDT into metrics of percent of drawing (%Ink) versus non-drawing (%Think) time. We also obtained composite Z-scores of cognition, including attention/information processing (AIP), to explore associations of %Ink and %Think times to cognitive and motor performance. Despite equivalent TTC, %Ink and %Think Command times (Copy n.s.) were significant (AgeXAffect interaction: p=.03)—younger depressed spent a smaller proportion of time drawing relative to thinking compared to the older depressed group. Command %Think time negatively correlated with AIP in the older depressed group (r=−.46; p=.02). Copy %Think time negatively correlated with AIP in the younger depressed (r=−.47; p=.03) and older euthymic groups (r=−.51; p=.01). The dCDT differentiated aspects of psychomotor slowing in depression regardless of age, while dCDT/cognitive associates for younger adults with depression mimicked patterns of older euthymics.

ObjectiveVerbal memory difficulties are common among individuals with late-life depression (LLD), though there is limited knowledge about disruptions to underlying cerebral circuitry. The purpose of this study is to examine aberrations to cerebral networks implicated in encoding novel verbal semantic material among older adults with LLD.MethodsTwenty-four older adults with early-onset LLD and 23 non-depressed comparisons participated in the study. Participants completed a word list-learning task while undergoing functional magnetic resonance imaging.ResultsIn the context of equivalent recall and recognition of words following scanning and similar hippocampal volumes, patients with LLD exhibited less activation in structures known to be relevant for new learning and memory, including hippocampus, parahippocampal gyrus, insula, and cingulate, relative to non-ill comparisons. An important region in which the LLD group displayed greater activation than the non-depressed comparison group was in left inferior frontal gyrus, an area involved in cognitive control and controlled semantic/phonological retrieval and analysis; this region may be critical for LLD patients to consolidate encoded words into memory.ConclusionsFunctional irregularities found in LLD patients may reflect different modes of processing to-be-remembered information and/or early changes predictive of incipient cognitive decline. Future studies might consider mechanisms that could contribute to these functional differences, including hypothalamic-pituitary-adrenal axis functioning and vascular integrity, and utilize longitudinal designs in order to understand whether functional changes are predictive of incipient cognitive decline.