Multimodal brain connectivity analysis in unmedicated late-life depression

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

The need to change: Is there a critical role of midlife adaptation in mental health later in life?

Although late-life depression (LLD) is a serious health problem and more common than dementia in people over 60, it is underdiagnosed and undertreated. The cognitive-emotional etiology of LLD is particularly poorly understood. This is in contrast to the now extensive literature from psychology and cognitive neuroscience on the characteristics of emotionally healthy aging. This research consistently shows a change in emotional processing in older adults that is modulated by prefrontal regulation. Lifespan theories explain this change in terms of neurocognitive adaptation to limited opportunities and resources that typically occur in the second half of life. Epidemiological data on an increase in well-being after a low point around age 50 suggest that the majority of people seem quite capable of making this adaptation, even though empirical evidence for a causal modulation of this so called 'paradox of aging' and for the role of the midlife dip is still lacking. Intriguingly, LLD is associated with deficits in emotional, cognitive, and prefrontal functions similar to those shown to be crucial for healthy adaptation. Suspected causes of these deficits, such as white matter lesions or affective instability, become apparent as early as midlife when internal and external changes as well as daily challenges set in. Based on these findings, we propose that some individuals who develop depression at older ages may not have been able to successfully implement self-regulatory adaptation at midlife. Here, we review the current evidence and theories on successful aging, the neurobiology of LLD, and well-being across the lifespan. Drawing on recent advances in lifespan theories, emotion regulation research, and cognitive neuroscience, we propose a model of successful versus unsuccessful adaptation that emphasizes the increasing need for implicit habitual control and resource-based regulatory choice during midlife.

Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

Extracting BOLD signals based on time-constrained multiset canonical correlation analysis for brain functional network estimation and classification

Brain functional network (BFN), usually estimated from blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), has been proven to be a powerful tool to study the organization of the brain and discover biomarkers for diagnosis of brain disorders. Prior to BFN estimation and classification, extracting representative BOLD signals from brain regions of interest (ROIs) is a critical step. Traditional extraction methods include averaging, peaking operation and dimensionality reduction, often leading to signal cancellation and information loss. In this paper, we propose a novel method, namely time-constrained multiset canonical correlation analysis (TMCCA), to extract representative BOLD signals for subsequent BFN estimation and classification. Different from traditional methods that equally treat all BOLD signals in a ROI, the proposed method assigns weights to different BOLD signals, and learns the optimal weights to make the extracted representative signals jointly maximize the multiple correlations between ROIs. Importantly, time-constraint is incorporated into our proposed method, which can effectively encode nonlinear relationship among BOLD signals. To evaluate the effectiveness of the proposed method, the extracted BOLD signals is used to estimate BFN and, in turn, identify brain disorders, including mild cognitive impairment (MCI) and autistic spectrum disorder (ASD). Experimental results demonstrate that our proposed TMCCA can lead to better performance than traditional methods.

Association of White Matter Integrity With Executive Function and Antidepressant Treatment Outcome in Patients With Late-Life Depression

OBJECTIVE

While patients with late-life depression (LLD) often exhibit microstructural white matter alterations that can be identified with diffusion tensor imaging (DTI), there is a dearth of information concerning the links between DTI findings and specific cognitive performance, as well as between DTI measures and antidepressant treatment outcomes.

DESIGN

Neuroimaging and cognitive tests were conducted at baseline in 71 older adults participating in a larger, 8-week duration antidepressant randomized controlled trial. Correlations between DTI measures of white matter integrity evaluated with tract-based spatial statistics, baseline neurocognitive performance, and prospective antidepressant treatment outcome were evaluated.

RESULTS

Fractional anisotropy (FA), an index of white matter integrity, was significantly positively associated with better cognitive function as measured by the Initiation/Perseveration subscale of the Dementia Rating Scale in the bilateral superior longitudinal fasciculus (SLF), bilateral SLF-temporal, and right corticospinal tract (CST). An exploratory analysis limited to these tracts revealed that increased FA in the right CST, right SLF, and right SLF-temporal tracts was correlated with a greater decrease in depressive symptoms. Increased FA in the right CST predicted a greater chance of remission, while increased FA in the right CST and the right SLF predicted a greater chance of treatment response.

CONCLUSION

In late-life depression LLD subjects, white matter integrity was positively associated with executive function in white matter tracts which act as key connecting structures underlying the cognitive control network. These tracts may play a role as a positive prognostic factor in antidepressant treatment outcome.

Apathy: Neurobiology, Assessment and Treatment

Apathy is a highly prevalent, disabling and treatment resistant syndrome. It is defined as a quantitative reduction of goal- directed activity in comparison to the patient’s previous level of in multiple dimensions including behavior/cognition, emotion and social interaction. It has been described in major depressive disorder, Alzheimer’s disease, frontotemporal dementia, Parkinson’s Disease, cerebrovascular disease, and vascular dementia, among others. This review will address the neuropsychology and associated neurobiological underpinnings of apathy in the above conditions, identify specific methods to assess apathy clinically, and review the literature on managing apathy across these various disorders.

Neural substrates for late-life depression: A selective review of structural neuroimaging studies

Recent neuroimaging studies have characterized the pathophysiology of late-life depression (LLD) as a dysfunction of the brain networks involved in the regulation of emotion, motivational behavior, cognitive control, executive function, and self-referential thinking. In this article, we reviewed LLD-associated structural neuroimaging markers such as white matter hyperintensity (WMH), white matter integrity measured by diffusion tensor imaging, cortical and subcortical volumes, and cortical thickness, which may provide a structural basis for brain network dysfunction in LLD. LLD was associated with greater severity or volumes of deep, periventricular, or overall WMH and with decreased white matter integrity in the brain regions belonging to the fronto-striatal-limbic circuits and reduced white matter tract integrity which connects these circuits, such as the cingulum, corpus callosum, or uncinate fasciculus. Decreased volumes or cortical thickness in the prefrontal cortex, orbitofrontal cortex, anterior and posterior cingulate cortex, several temporal and parietal regions, hippocampus, amygdala, striatum, thalamus, and the insula were associated with LLD. These structural neuroimaging findings were also associated with cognitive dysfunction, which is a prominent clinical feature in LLD. Several structural neuroimaging markers including the WMH burden, white matter integrity, and cortical and subcortical volumes predicted antidepressant response in LLD. These structural neuroimaging findings support the hypothesis that disruption of the brain networks involved in emotion regulation and cognitive processing by impaired structural connectivity is strongly associated with the pathophysiology of LLD.

Sexual Dimorphism in the Brain Correlates of Adult-Onset Depression: A Pilot Structural and Functional 3T MRI Study

Major Depressive Disorder (MDD) is a disabling illness affecting more than 5% of the elderly population. Higher female prevalence and sex-specific symptomatology have been observed, suggesting that biologically-determined dimensions might affect the disease onset and outcome. Rumination and executive dysfunction characterize adult-onset MDD, but sex differences in these domains and in the related brain mechanisms are still largely unexplored. The present pilot study aimed to explore any interactions between adult-onset MDD and sex on brain morphology and brain function during a Go/No-Go paradigm. We hypothesized to detect diagnosis by sex effects on brain regions involved in self-referential processes and cognitive control. Twenty-four subjects, 12 healthy (HC) (mean age 68.7 y, 7 females and 5 males) and 12 affected by adult-onset MDD (mean age 66.5 y, 5 females and 7 males), underwent clinical evaluations and a 3T magnetic resonance imaging (MRI) session. Diagnosis and diagnosis by sex effects were assessed on regional gray matter (GM) volumes and task-related functional MRI (fMRI) activations. The GM volume analyses showed diagnosis effects in left mid frontal cortex (p < 0.01), and diagnosis by sex effects in orbitofrontal, olfactory, and calcarine regions (p < 0.05). The Go/No-Go fMRI analyses showed MDD effects on fMRI activations in left precuneus and right lingual gyrus, and diagnosis by sex effects on fMRI activations in right parahippocampal gyrus and right calcarine cortex (p < 0.001, ≥ 40 voxels). Our exploratory results suggest the presence of sex-specific brain correlates of adult-onset MDD-especially in regions involved in attention processing and in the brain default mode-potentially supporting cognitive and symptom differences between sexes.

Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer's dementia, and normal cognition

A history of depression is a risk factor for dementia. Despite strong epidemiologic evidence, the pathways linking depression and dementia remain unclear. We assessed structural brain alterations in white and gray matter of frontal-executive and corticolimbic circuitries in five groups of older adults putatively at-risk for developing dementia- remitted depression (MDD), non-amnestic MCI (naMCI), MDD+naMCI, amnestic MCI (aMCI), and MDD+aMCI. We also examined two other groups: non-psychiatric ("healthy") controls (HC) and individuals with Alzheimer's dementia (AD). Magnetic resonance imaging (MRI) data were acquired on the same 3T scanner. Following quality control in these seven groups, from diffusion-weighted imaging (n = 300), we compared white matter fractional anisotropy (FA), mean diffusivity (MD), and from T1-weighted imaging (n = 333), subcortical volumes and cortical thickness in frontal-executive and corticolimbic regions of interest (ROIs). We also used exploratory graph theory analysis to compare topological properties of structural covariance networks and hub regions. We found main effects for diagnostic group in FA, MD, subcortical volume, and cortical thickness. These differences were largely due to greater deficits in the AD group and to a lesser extent aMCI compared with other groups. Graph theory analysis revealed differences in several global measures among several groups. Older individuals with remitted MDD and naMCI did not have the same white or gray matter changes in the frontal-executive and corticolimbic circuitries as those with aMCI or AD, suggesting distinct neural mechanisms in these disorders. Structural covariance global metrics suggested a potential difference in brain reserve among groups.

Vascular depression for radiology: A review of the construct, methodology, and diagnosis

Vascular depression (VD) as defined by magnetic resonance imaging (MRI) has been proposed as a unique subtype of late-life depression. The VD hypothesis posits that cerebrovascular disease, as characterized by the presence of MRI-defined white matter hyperintensities, contributes to and increases the risk for depression in older adults. VD is also accompanied by cognitive impairment and poor antidepressant treatment response. The VD diagnosis relies on MRI findings and yet this clinical entity is largely unfamiliar to neuroradiologists and is rarely, if ever, discussed in radiology journals. The primary purpose of this review is to introduce the MRI-defined VD construct to the neuroradiology community. Case reports are highlighted in order to illustrate the profile of VD in terms of radiological, clinical, and neuropsychological findings. A secondary purpose is to elucidate and elaborate on the measurement of cerebrovascular disease through visual rating scales and semi- and fully-automated volumetric methods. These methods are crucial for determining whether lesion burden or lesion severity is the dominant pathological contributor to VD. Additionally, these rating methods have implications for the growing field of computer assisted diagnosis. Since VD has been found to have a profile that is distinct from other types of late-life depression, neuroradiologists, in conjunction with psychiatrists and psychologists, should consider VD in diagnosis and treatment planning.

[The cognitive-behavioural impact of hypertension]

Arterial hypertension is considered the main modifiable vascular risk factor that causes silent damage to brain vessels. This vascular brain injury could be the common nucleus that justifies the cognitive (cognitive impairment, dementia and Alzheimer's disease) and behavioural symptoms (late-life depression) of target organ damage mediated-hypertension. Incomplete knowledge about the complex pathophysiology that links hypertension with cognitive-behavioural changes is overlooking brain involvement and underestimating cardio and cerebrovascular risk. The confluence of cognitive impairment, depression and arterial hypertension in elderly adults, warns of the need for a comprehensive evaluation to plan treatment, improve prognosis and contribute to reducing the risk of dementia and its incidence.

Evidence for Structural and Functional Alterations of Frontal-Executive and Corticolimbic Circuits in Late-Life Depression and Relationship to Mild Cognitive Impairment and Dementia: A Systematic Review

Depression is a risk factor for developing Alzheimer's disease and Related Dementia (ADRD). We conducted a systematic review between 2008 and October 2018, to evaluate the evidence for a conceptual mechanistic model linking depression and ADRD, focusing on frontal-executive and corticolimbic circuits. We focused on two neuroimaging modalities: diffusion-weighted imaging measuring white matter tract disruptions and resting-state functional MRI measuring alterations in network dynamics in late-life depression (LLD), mild cognitive impairment (MCI), and LLD+MCI vs. healthy control (HC) individuals. Our data synthesis revealed that in some but not all studies, impairment of both frontal-executive and corticolimbic circuits, as well as impairment of global brain topology was present in LLD, MCI, and LLD+MCI vs. HC groups. Further, posterior midline regions (posterior cingulate cortex and precuneus) appeared to have the most structural and functional alterations in all patient groups. Future cohort and longitudinal studies are required to address the heterogeneity of findings, and to clarify which subgroups of people with LLD are at highest risk for developing MCI and ADRD.

Functional connectivity predictors of acute depression treatment outcome

Few studies have examined functional connectivity (FC) patterns using functional magnetic resonance imaging (fMRI) to predict outcomes in late-life depression. We hypothesized that FC within and between frontal and limbic regions would be associated with 12-week depression outcome in older depressed adults. Seventy-one subjects with major depression were enrolled in the study. A study geriatric psychiatrist performed a clinical interview and completed a Montgomery-Åsberg Depression Rating Scale (MADRS). All study participants were free of medication at baseline and had a brain fMRI scan. Using a regions of interest (ROI) atlas (including 164 ROIs), we conducted ROI-to-ROI resting-state FC analyses for each participant. In terms of treatment participants were offered sertraline initially, although in this naturalistic study, other medications were also prescribed. Subjects were evaluated every 2 weeks up to 12 weeks by the study psychiatrist, who followed a flexible, clinically based medication dosing schedule. Multivariate regression analysis was used to examine correlation between change of MADRS score over 12 weeks and baseline FC between brain regions, controlling for age, gender, mean head motion, and baseline MADRS. We found greater FC between the left inferior frontal gyrus pars triangularis and the left frontal eye field and FC of these two regions with a number of brain regions related to reward, salience, and sensorimortor function were correlated with change in MADRS score over 12 weeks. Our results highlight the important role of between inner speech-reward, attention-salience, and attention-sensorimotor network synchronization in predicting acute treatment response in late-life depression.

The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding

Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R² = 0.18; Replication: adj. R² = 0.22; MSE = 0.06, Predictive R² = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R² = 0.21; Replication: adj. R² = 0.18; MSE = 0.03, Predictive R² = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.

Differential patterns of age-related cortical and subcortical functional connectivity in 6-to-10 year old children: A connectome-wide association study

INTRODUCTION

Typical brain development is characterized by specific patterns of maturation of functional networks. Cortico-cortical connectivity generally increases, whereas subcortico-cortical connections often decrease. Little is known about connectivity changes amongst different subcortical regions in typical development.

METHODS

This study examined age- and gender-related differences in functional connectivity between and within cortical and subcortical regions using two different approaches. The participants included 411 six- to ten-year-old typically developing children sampled from the population-based Generation R study. Functional connectomes were defined in native space using regions of interest from subject-specific FreeSurfer segmentations. Connections were defined as: (a) the correlation between regional mean time-series; and (b) the focal maximum of voxel-wise correlations within FreeSurfer regions. The association of age and gender with each functional connection was determined using linear regression. The preprocessing included the exclusion of children with excessive head motion and scrubbing to reduce the influence of minor head motion during scanning.

RESULTS

Cortico-cortical associations echoed previous findings that connectivity shifts from short to long-range with age. Subcortico-cortical associations with age were primarily negative in the focal network approach but were both positive and negative in the mean time-series network approach. Between subcortical regions, age-related associations were negative in both network approaches. Few connections had significant associations with gender.

CONCLUSIONS

The present study replicates previously reported age-related patterns of connectivity in a relatively narrow age-range of children. In addition, we extended these findings by demonstrating decreased connectivity within the subcortex with increasing age. Lastly, we show the utility of a more focal approach that challenges the spatial assumptions made by the traditional mean time series approach.

Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression

Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10-30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke.

Advances and Barriers for Clinical Neuroimaging in Late-Life Mood and Anxiety Disorders

PURPOSE OF REVIEW

Mood and anxiety disorders are very commonly experienced by older adults and are becoming a growing concern due to the rapidly aging global population. Recent advances in neuroimaging may help in improving outcomes in late-life mood and anxiety disorders. The elucidation of mechanisms contributing to late-life mental health disorders may ultimately lead to the identification of novel therapeutic interventions. Alternatively, clinically validated imaging biomarkers may allow for the prediction of treatment response and identification of better therapeutic approaches in late-life mood and anxiety disorders.

RECENT FINDINGS

In community samples, late-life depression and late-life generalized anxiety disorder occur up to 38 and 15%, respectively, while late-life bipolar disorder is less common and occur in approximately 0.5% of the population. There are significant challenges in treating and improving outcome in late-life mood and anxiety disorders. Time to treatment response and treatment resistance are increased in older adults. Novel neuroimaging techniques have the potential to improve diagnostic and therapeutic outcome in late-life mood and anxiety disorders either through "personalized pharmacotherapy" or through identifying dysfunction regions/networks to be subsequently used for direct interventions such as transcranial magnetic stimulation. This review will provide an overview of recent literature that substantiates the potential role of neuroimaging in clinical practice, as well as the barriers that must be overcome prior to clinical translation.

Association between change in brain gray matter volume, cognition, and depression severity: Pre- and post- antidepressant pharmacotherapy for late-life depression

Late-life depression (LLD) is associated with cognitive impairments and reduced gray matter volume (GMV); however the mechanisms underlying this association are not well understood. The goal of this study was to characterize changes in depression severity, cognitive function, and brain structure associated with pharmacologic antidepressant treatment for LLD. We administered a detailed neurocognitive battery and conducted structural magnetic resonance imaging (MRI) on 26 individuals with LLD, pre-/post-a 12-week treatment trial with venlafaxine. After calculating changes in cognitive performance, GMV, and depression severity, we calculated Pearson's correlations, performed permutation testing, and false discovery rate correction. We found that loss of GMV over 12 weeks in the superior orbital frontal gyrus was associated with less improvement in depression severity and that increased GMV in the same was associated with greater improvement in depression severity. We detected no associations between changes in cognitive performance and improvements in either depressive symptoms or changes in GMV.

Late-Life Depression: Modifications of Brain Resting State Activity

Late-life depression (LLD) is a common emotional and mental disability in the elderly population characterized by the presence of depressed mood, the loss of interest or pleasure in daily activities, and other depression symptoms. It has a serious effect on the quality of life of elderly individuals and increases their risk of developing physical and mental diseases. It is an important area of research, given the growing elderly population. Brain functional connectivity modifications represent one of the neurobiological biomarker for LLD even if to date remains poorly understood. In our study, we enrolled 10 elderly patients with depressive symptoms compared to 11 age-matched healthy controls. All participants were evaluated by means of neuropsychological tests and underwent the same functional magnetic resonance imaging (fMRI) protocol to evaluate modifications of brain resting state functional connectivity. Between-group differences were observed for the Geriatric Depression Scale and Hamilton Depression Rating Scale, with higher scores for patients with LLD. Voxel-wise, 1-way analysis of variance revealed between-group differences in left frontoparietal network (lFPN) and sensory motor network (SMN): Increased intrinsic connectivity in the LLD group was observed in the left dorsolateral prefrontal cortex and in the left superior parietal lobule of the lFPN and increased intrinsic connectivity in the LLD group was observed in the bilateral primary somatosensory cortex of the SMN. Our findings support the use of resting state fMRI as a potential biomarker for LLD; even if to confirm the relationship between brain changes and the pathophysiology of LLD, longitudinal neuroimaging studies are required.

Highways of the emotional intellect: white matter microstructural correlates of an ability-based measure of emotional intelligence

Individuals differ in their ability to understand emotional information and apply that understanding to make decisions and solve problems effectively - a construct known as Emotional Intelligence (EI). While considerable evidence supports the importance of EI in social and occupational functioning, the neural underpinnings of this capacity are relatively unexplored. We used Tract-Based Spatial Statistics (TBSS) to determine the white matter correlates of EI as measured by the ability-based Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Participants included 32 healthy adults (16 men; 16 women), aged 18-45 years. White matter integrity in key tracts was positively correlated with the Strategic Area branches of the MSCEIT (Understanding Emotions and Managing Emotions), but not the Experiential branches (Perceiving and Facilitating Emotions). Specifically, the Understanding Emotions branch was associated with greater fractional anisotropy (FA) within somatosensory and sensory-motor fiber bundles, particularly those of the left superior longitudinal fasciculus and corticospinal tract. Managing Emotions was associated with greater FA within frontal-affective association tracts including the anterior forceps and right uncinate fasciculus, along with frontal-parietal cingulum and interhemispheric corpus callosum tracts. These findings suggest that specific components of EI are directly related to the structural microarchitecture of major axonal pathways.

Brain structural connectivity in late-life major depressive disorder

Disrupted brain connectivity might explain both the pathogenesis and consequences of late-life major depressive disorder (LLD). However, it remains difficult to ascertain whether and how specific circuits are affected. We reviewed literature regarding brain connectivity in LLD, and we specifically focused on the role of structural pathology. LLD is associated with greater levels of cerebrovascular disease, and greater levels of cerebrovascular disease are associated with both depression development and treatment responsiveness. Cerebrovascular disease is most often measured as white matter hyperintensity (WMH) burden, and histopathology studies suggest WMH reflect myelin damage and fluid accumulation (among other underlying pathology). WMHs appear as confluent caps around the ventricles (periventricular), as well as isolated lesions in the deep white matter. The underlying tissue damage and implications for brain connectivity may differ by WMH location or severity. WMHs are associated with lower white matter microstructural integrity (measured with diffusion tensor imaging) and altered brain function (measured with functional MRI). LLD is also associated with lower white matter microstructural integrity and grey matter loss which may also alter the network properties and function of the brain. Damage to brain structure reflected by WMH, reduced white matter microstructural integrity, and atrophy may affect brain function, and are therefore likely pathophysiological mechanisms of LLD. Additional research is needed to fully characterize the developmental course and pathology underlying these imaging markers, and to understand how structural damage explains LLD's various clinical manifestations.

Biophysical changes in subcortical nuclei: the impact of diabetes and major depression

Magnetization transfer (MT) is a neuroimaging technique that is frequently used to characterize the biophysical abnormalities in both gray and white matter regions of the brain. In our study, we used MT to examine the integrity of key nodes in frontal-subcortical circuits in four subject groups: patients diagnosed with type 2 diabetes with and without major depression (MDD), a healthy control group, and a group diagnosed with MDD without diabetes. In the MDD group, MT studies demonstrated lower magnetization transfer ratios (MTR), a marker of abnormalities in the macromolecular protein pool, in the thalami when compared with the control groups. The group with diabetes and MDD showed lower MTR in the globus pallidus when compared with the group with MDD. Biophysical measures, in subcortical nuclei, correlated inversely with measures of glycemic control, cerebrovascular burden and depression scores. These findings have broad implications for the underlying neuronal circuitry and neurobiology of mood disorders.

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

Clinical, cognitive, and functional connectivity correlations of resting-state intrinsic brain activity alterations in unmedicated depression

The pervasive and persistent nature of depressive symptoms has made resting-state functional magnetic resonance imaging (rs-fMRI) an appropriate approach for understanding the underlying mechanisms of major depressive disorder. The majority of rs-fMRI research has focused on depression-related alterations in the interregional coordination of brain baseline low frequency oscillations (LFOs). However, alteration of the regional amplitude of LFOs in depression, particularly its clinical, cognitive and network implications, has not been examined comprehensively yet. rs-fMRI amplitudes of low-frequency fluctuation (ALFF/fALFF) mediated by two LFO bands of 0.01-0.08 Hz (LF-ALFF/fALFF) and 0.1-0.25 Hz (HF-ALFF/fALFF) were measured in unmedicated subjects with major depressive disorder (n=20) and a healthy control group (n=25). A novel method of "ALFF-based functional connectivity" analysis was developed to test regional/network interaction abnormalities in depression. Our results revealed abnormal alterations in ALFF for both lower and higher frequency bands of LFOs in regions that participate in affective networks, corticostriatal circuits and motor/somatosensory networks. A strong positive correlation was detected between depressive symptom severity and fALFF in the anterior cingulate cortex. Functional connectivity of the thalamus and postcentral area with altered ALFF were found to be decreased with other interacting regions of their involved networks. Major depressive disorder relates to the alterations of regional properties of intrinsic neural activity with meaningful clinical and cognitive correlations. This study also proposes an integrating regional/network dysfunction in MDD.

Alcohol binge drinking during adolescence or dependence during adulthood reduces prefrontal myelin in male rats

Teen binge drinking is associated with low frontal white matter integrity and increased risk of alcoholism in adulthood. This neuropathology may result from alcohol exposure or reflect a pre-existing condition in people prone to addiction. Here we used rodent models with documented clinical relevance to adolescent binge drinking and alcoholism in humans to test whether alcohol damages myelinated axons of the prefrontal cortex. In Experiment 1, outbred male Wistar rats self-administered sweetened alcohol or sweetened water intermittently for 2 weeks during early adolescence. In adulthood, drinking behavior was tested under nondependent conditions or after dependence induced by 1 month of alcohol vapor intoxication/withdrawal cycles, and prefrontal myelin was examined 1 month into abstinence. Adolescent binge drinking or adult dependence induction reduced the size of the anterior branches of the corpus callosum, i.e., forceps minor (CCFM), and this neuropathology correlated with higher relapse-like drinking in adulthood. Degraded myelin basic protein in the gray matter medial to the CCFM of binge rats indicated myelin was damaged on axons in the mPFC. In follow-up studies we found that binge drinking reduced myelin density in the mPFC in adolescent rats (Experiment 2) and heavier drinking predicted worse performance on the T-maze working memory task in adulthood (Experiment 3). These findings establish a causal role of voluntary alcohol on myelin and give insight into specific prefrontal axons that are both sensitive to alcohol and could contribute to the behavioral and cognitive impairments associated with early onset drinking and alcoholism.