Lihong Wang

Identification of MCI individuals using structural and functional connectivity networks.

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimers disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.

Prefrontal mechanisms for executive control over emotional distraction are altered in major depression

A dysfunction in the interaction between executive function and mood regulation has been proposed as the pathophysiology of depression. However, few studies have investigated the alteration in brain systems related to executive control over emotional distraction in depression. To address this issue, 19 patients with major depressive disorder (MDD) and 20 healthy controls were scanned using functional magnetic resonance imaging. Participants performed an emotional oddball task in which infrequently presented circle targets required detection while sad and neutral pictures were irrelevant novel distractors. Hemodynamic responses were compared for targets, sad distractors, and for targets that followed sad or neutral distractors (Target-after-Sad and Target-after-Neutral). Patients with MDD revealed attenuated activation overall to targets in executive brain regions. Behaviorally, MDD patients were slower in response to Target-after-Sad than Target-after-Neutra stimuli. Patients also revealed a reversed activation pattern from controls in response to this contrast in the left anterior cingulate, insula, right inferior frontal gyrus (IFG), and bilateral middle frontal gyrus. Those patients who engaged the right IFG more during Target-after-Neutral stimuli responded faster to targets, confirming a role of this region in coping with emotional distraction. The results provide direct evidence of an alteration in the neural systems that interplay cognition with mood in MDD.

Enriched white matter connectivity networks for accurate identification of MCI patients.

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimers disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities(λ(1), λ(2), and λ(3)), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using an SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients.

Scan–rescan reliability of subcortical brain volumes derived from automated segmentation

Large‐scale longitudinal studies of regional brain volume require reliable quantification using automated segmentation and labeling. However, repeated MR scanning of the same subject, even if using the same scanner and acquisition parameters, does not result in identical images due to small changes in image orientation, changes in prescan parameters, and magnetic field instability. These differences may lead to appreciable changes in estimates of volume for different structures. This study examined scan–rescan reliability of automated segmentation algorithms for measuring several subcortical regions, using both within‐day and across‐day comparison sessions in a group of 23 normal participants. We found that the reliability of volume measures including percent volume difference, percent volume overlap (Dices coefficient), and intraclass correlation coefficient (ICC), varied substantially across brain regions. Low reliability was observed in some structures such as the amygdala (ICC = 0.6), with higher reliability (ICC = 0.9) for other structures such as the thalamus and caudate. Patterns of reliability across regions were similar for automated segmentation with FSL/FIRST and FreeSurfer (longitudinal stream). Reliability was associated with the volume of the structure, the ratio of volume to surface area for the structure, the magnitude of the interscan interval, and the method of segmentation. Sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region. Thus, longitudinal research requires a careful analysis of sample size and choice of segmentation method combined with a consideration of the brain structure(s) of interest and the magnitude of the anticipated effects. Hum Brain Mapp, 2010.

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work.

Amygdala Activation to Sad Pictures During High-Field (4 Tesla) Functional Magnetic Resonance Imaging

Fear-related processing in the amygdala has been well documented, but its role in signaling other emotions remains controversial. The authors recovered signal loss in the amygdala at high-field strength using an inward spiral pulse sequence and probed its response to pictures varying in their degree of portrayed sadness. These pictures were presented as intermittent task-irrelevant distractors during a concurrent visual oddball task. Relative to neutral distractors, sad distractors elicited greater activation along ventral brain regions, including the amygdala, fusiform gyrus, and inferior frontal gyrus. In contrast, oddball targets engaged dorsal sectors of frontal, parietal, and cingulate cortices. The amygdalas role in emotional evaluation thus extends to images of grief and despair as well as to those depicting violence and threat.

Psychological and Neural Mechanisms of Trait Mindfulness in Reducing Depression Vulnerability

Mindfulness-based interventions are effective for reducing depressive symptoms. However, the psychological and neural mechanisms are unclear. This study examined which facets of trait mindfulness offer protection against negative bias and rumination, which are key risk factors for depression. Nineteen male volunteers completed a 2-day functional magnetic resonance imaging study. One day utilized a stress-induction task and the other day utilized a mindful breathing task. An emotional inhibition task was used to measure neural and behavioral changes related to state negative bias, defined by poorer performance in inhibiting negative relative to neutral stimuli. Associations among trait mindfulness [measured by the Five Facet Mindfulness Questionnaire (FFMQ)], trait rumination, and negative bias were examined. Non-reactivity scores on the FFMQ correlated negatively with rumination and negative bias following the stress induction. Non-reactivity was inversely correlated with insula activation during inhibition to negative stimuli after the mindful breathing task. Our results suggest non-reactivity to inner experience is the key facet of mindfulness that protects individuals from psychological risk for depression. Based on these results, mindfulness could reduce vulnerability to depression in at least two ways: (i) by buffering against trait rumination and negative bias and (ii) by reducing automatic emotional responding via the insula.

Opposing influences of emotional and non-emotional distracters upon sustained prefrontal cortex activity during a delayed-response working memory task

Performance in delayed-response working memory (WM) tasks is typically associated with sustained activation in the dorsolateral prefrontal cortex (dlPFC) that spans the delay between the memoranda and the memory probe. Recent studies have demonstrated that novel distracters presented during the delay interval both affect sustained activation and impair WM performance. However, the effect of the performance-impairing distracters upon sustained dlPFC delay activity was related to the characteristics of the distracters: memoranda-confusable distracters increased delay activity, whereas memoranda-nonconfusable emotional distracters decreased delay activity. Because these different effects were observed in different studies, it is possible that different dlPFC regions were involved and the paradox is more apparent than real. To investigate this possibility, event-related fMRI data were recorded while subjects performed a WM task for faces with memoranda-confusable (novel faces) and memoranda-nonconfusable emotional (novel scenes) distracters presented during the delay interval. Consistent with previous findings, confusable face distracters increased dlPFC delay activity, while nonconfusable emotional distracters decreased dlPFC delay activity, and these opposing effects modulated activity in the same dlPFC regions. These results provide direct evidence that specific regions of the dlPFC are generally involved in mediating the effects of distraction, while showing sensitivity to the nature of distraction. These findings are relevant for understanding alterations in the neural mechanisms associated with both general impairment of cognitive control and with specific impairment in the ability to control emotional distraction, such as those observed in aging and affective disorders, respectively.

Prognostic Value of Posteromedial Cortex Deactivation in Mild Cognitive Impairment

Background Normal subjects deactivate specific brain regions, notably the posteromedial cortex (PMC), during many tasks. Recent cross-sectional functional magnetic resonance imaging (fMRI) data suggests that deactivation during memory tasks is impaired in Alzheimers disease (AD). The goal of this study was to prospectively determine the prognostic significance of PMC deactivation in mild cognitive impairment (MCI). Methodology/Principal Findings 75 subjects (34 MCI, 13 AD subjects and 28 controls) underwent baseline fMRI scanning during encoding of novel and familiar face-name pairs. MCI subjects were followed longitudinally to determine conversion to AD. Regression and analysis of covariance models were used to assess the effect of PMC activation/deactivation on conversion to dementia as well as in the longitudinal change in dementia measures. At longitudinal follow up of up to 3.5 years (mean 2.5±0.79 years), 11 MCI subjects converted to AD. The proportion of deactivators was significantly different across all groups: controls (79%), MCI-Nonconverters (73%), MCI-converters (45%), and AD (23%) (p<0.05). Mean PMC activation magnitude parameter estimates, at baseline, were negative in the control (−0.57±0.12) and MCI-Nonconverter (−0.33±0.14) groups, and positive in the MCI-Converter (0.37±0.40) and AD (0.92±0.30) groups. The effect of diagnosis on PMC deactivation remained significant after adjusting for age, education and baseline Mini-Mental State Exam (p<0.05). Baseline PMC activation magnitude was correlated with change in dementia ratings from baseline. Conclusion Loss of physiological functional deactivation in the PMC may have prognostic value in preclinical AD, and could aid in profiling subgroups of MCI subjects at greatest risk for progressive cognitive decline.

Altered Cerebellar-Cerebral Functional Connectivity in Geriatric Depression

Although volumetric and activation changes in the cerebellum have frequently been reported in studies on major depression, its role in the neural mechanism of depression remains unclear. To understand how the cerebellum may relate to affective and cognitive dysfunction in depression, we investigated the resting-state functional connectivity between cerebellar regions and the cerebral cortex in samples of patients with geriatric depression (n = 11) and healthy controls (n = 18). Seed-based connectivity analyses were conducted using seeds from cerebellum regions previously identified as being involved in the executive, default-mode, affective-limbic, and motor networks. The results revealed that, compared with controls, individuals with depression show reduced functional connectivity between several cerebellum seed regions, specifically those in the executive and affective-limbic networks with the ventromedial prefrontal cortex (vmPFC) and increased functional connectivity between the motor-related cerebellum seed regions with the putamen and motor cortex. We further investigated whether the altered functional connectivity in depressed patients was associated with cognitive function and severity of depression. A positive correlation was found between the Crus II–vmPFC connectivity and performance on the Hopkins Verbal Learning Test-Revised delayed memory recall. Additionally, the vermis–posterior cinglate cortex (PCC) connectivity was positively correlated with depression severity. Our results suggest that cerebellum–vmPFC coupling may be related to cognitive function whereas cerebellum–PCC coupling may be related to emotion processing in geriatric depression.