Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism.

Conditioned changes in the emotional response to threat (e.g. aversive unconditioned stimulus; UCS) are mediated in part by the prefrontal cortex (PFC). Unpredictable threats elicit large emotional responses, while the response is diminished when the threat is predictable. A better understanding of how PFC connectivity to other brain regions varies with threat predictability would provide important insights into the neural processes that mediate conditioned diminution of the emotional response to threat. The present study examined brain connectivity during predictable and unpredictable threat exposure using a fear conditioning paradigm (previously published in Wood et al., 2012) in which unconditioned functional magnetic resonance imaging data was reanalyzed to assess effective connectivity. Granger causality analysis was performed using the time series data from 15 activated regions of interest after hemodynamic deconvolution, to determine regional effective connectivity. In addition, connectivity path weights were correlated with trait anxiety measures to assess the relationship between negative affect and brain connectivity. Results indicate the dorsomedial PFC (dmPFC) serves as a neural hub that influences activity in other brain regions when threats are unpredictable. In contrast, the dorsolateral PFC (dlPFC) serves as a neural hub that influences the activity of other brain regions when threats are predictable. These findings are consistent with the view that the dmPFC coordinates brain activity to take action, perhaps in a reactive manner, when an unpredicted threat is encountered, while the dlPFC coordinates brain regions to take action, in what may be a more proactive manner, to respond to predictable threats. Further, dlPFC connectivity to other brain regions (e.g. ventromedial PFC, amygdala, and insula) varied with negative affect (i.e. trait anxiety) when the UCS was predictable, suggesting that stronger connectivity may be required for emotion regulation in individuals with higher levels of negative affect.

These results support the notion that memory disruption in schizophrenia might originate from hippocampal dysfunction and that medication restores some aspects of fronto-temporal dysconnectivity. Patterns of fronto-temporal connectivity could provide valuable biomarkers to identify new treatments for the symptoms of schizophrenia, including memory deficits.

We have proposed that haptic activation of the shape-selective lateral occipital complex (LOC) reflects a model of multisensory object representation in which the role of visual imagery is modulated by object familiarity. Supporting this, a previous functional magnetic resonance imaging (fMRI) study from our laboratory used inter-task correlations of blood oxygenation level-dependent (BOLD) signal magnitude and effective connectivity (EC) patterns based on the BOLD signals to show that the neural processes underlying visual object imagery (objIMG) are more similar to those mediating haptic perception of familiar (fHS) than unfamiliar (uHS) shapes. Here we employed fMRI to test a further hypothesis derived from our model, that spatial imagery (spIMG) would evoke activation and effective connectivity patterns more related to uHS than fHS. We found that few of the regions conjointly activated by spIMG and either fHS or uHS showed inter-task correlations of BOLD signal magnitudes, with parietal foci featuring in both sets of correlations. This may indicate some involvement of spIMG in HS regardless of object familiarity, contrary to our hypothesis, although we cannot rule out alternative explanations for the commonalities between the networks, such as generic imagery or spatial processes. EC analyses, based on inferred neuronal time series obtained by deconvolution of the hemodynamic response function from the measured BOLD time series, showed that spIMG shared more common paths with uHS than fHS. Re-analysis of our previous data, using the same EC methods as those used here, showed that, by contrast, objIMG shared more common paths with fHS than uHS. Thus, although our model requires some refinement, its basic architecture is supported: a stronger relationship between spIMG and uHS compared to fHS, and a stronger relationship between objIMG and fHS compared to uHS.

ObjectiveTo investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD).MethodsWe employed a model-free data-driven approach for computing whole-brain WM topologic arrangement and connectivity strength between brain regions by utilizing diffusion MRI of 70 participants with early-stage drug-naive PD and 41 healthy controls. Subsequently, we generated a novel group-specific WM anatomical network by minimizing variance in anatomical connectivity of each group. Global WM connectivity strength and network measures were computed on this group-specific WM anatomical network and were compared between the groups. We tested correlations of these network measures with clinical measures in PD to assess their pathophysiologic relevance.ResultsPD-relevant cortical and subcortical regions were identified in the novel PD-specific WM anatomical network. Impaired modular organization accompanied by a correlation of network measures with multiple clinical variables in early PD were revealed. Furthermore, disease duration was negatively correlated with global connectivity strength of the PD-specific WM anatomical network.ConclusionBy minimizing variance in anatomical connectivity, this study found the presence of a novel WM structural connectome in early PD that correlated with clinical symptoms, despite the lack of a priori analytic assumptions. This included the novel finding of increased structural connectivity between known PD-relevant brain regions. The current study provides a framework for further investigation of WM structural changes underlying the clinical and pathologic heterogeneity of PD.

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity which is modeled as a convolution of the latent neuronal response and the hemodynamic response function (HRF). Since the sources of HRF variability can be nonneural in nature, the measured fMRI signal does not faithfully represent underlying neural activity. Therefore, it is advantageous to deconvolve the HRF from the fMRI signal. However, since both latent neural activity and the voxel-specific HRF is unknown, the deconvolution must be blind. Existing blind deconvolution approaches employ highly parameterized models, and it is unclear whether these models have an over fitting problem. In order to address these issues, we 1) present a nonparametric deconvolution method based on homomorphic filtering to obtain the latent neuronal response from the fMRI signal and, 2) compare our approach to the best performing existing parametric model based on the estimation of the biophysical hemodynamic model using the Cubature Kalman Filter/Smoother. We hypothesized that if the results from nonparametric deconvolution closely resembled that obtained from parametric deconvolution, then the problem of over fitting during estimation in highly parameterized deconvolution models of fMRI could possibly be over stated. Both simulations and experimental results demonstrate support for our hypothesis since the estimated latent neural response from both parametric and nonparametric methods were highly correlated in the visual cortex. Further, simulations showed that both methods were effective in recovering the simulated ground truth of the latent neural response.

Purpose To investigate whether combining multiple magnetic resonance (MR) imaging modalities such as T1-weighted and diffusion-weighted MR imaging could reveal imaging biomarkers associated with cognition in active professional fighters. Materials and Methods Active professional fighters (n = 297; 24 women and 273 men) were recruited at one center. Sixty-two fighters (six women and 56 men) returned for a follow-up examination. Only men were included in the main analysis of the study. On the basis of computerized testing, fighters were separated into the cognitively impaired and nonimpaired groups on the basis of computerized testing. T1-weighted and diffusion-weighted imaging were performed, and volume and cortical thickness, along with diffusion-derived metrics of 20 major white matter tracts were extracted for every subject. A classifier was designed to identify imaging biomarkers related to cognitive impairment and was tested in the follow-up dataset. Results The classifier allowed identification of seven imaging biomarkers related to cognitive impairment in the cohort of active professional fighters. Areas under the curve of 0.76 and 0.69 were obtained at baseline and at follow-up, respectively, with the optimized classifier. The number of years of fighting had a significant (P = 8.8 × 10) negative association with fractional anisotropy of the forceps major (effect size [d] = 0.34) and the inferior longitudinal fasciculus (P = .03; d = 0.17). A significant difference was observed between the impaired and nonimpaired groups in the association of fractional anisotropy in the forceps major with number of fights (P = .03, d = 0.38) and years of fighting (P = 6 × 10, d = 0.63). Fractional anisotropy of the inferior longitudinal fasciculus was positively associated with psychomotor speed (P = .04, d = 0.16) in nonimpaired fighters but no association was observed in impaired fighters. Conclusion Without enforcement of any a priori assumptions on the MR imaging-derived measurements and with a multivariate approach, the study revealed a set of seven imaging biomarkers that were associated with cognition in active male professional fighters. RSNA, 2017 Online supplemental material is available for this article.

Failure of the circuit implicated in emotion regulation was associated with a significant history of ELT but not with MDD more broadly. Non-ELT related depression was associated with intact regulation of emotion despite the absence of difference in severity of illness. These findings indicate opposing system-level differences within depression relative to ELT are expressed as differential amygdala reactivity.