Cortical thickness is related to cognitive-motor automaticity and attention allocation in individuals with Alzheimer's disease: a regions of interest study.

Alzheimer's disease (AD) is characterized by a distinct pattern of cortical thinning and resultant changes in cognition and function. These result in prominent deficits in cognitive-motor automaticity. The relationship between AD-related cortical thinning and decreased automaticity is not well-understood. We aimed to investigate the relationship between cortical thickness regions-of-interest (ROI) and automaticity and attention allocation in AD using hypothesis-driven and exploratory approaches. We performed an ROI analysis of 46 patients with AD. Data regarding MR images, demographic characteristics, cognitive-motor dual task performance, and cognition were extracted from medical records. Cortical thickness was calculated from MR T1 images using FreeSurfer. Data from the dual task assessment was used to calculate the combined dual task effect (cDTE), a measure of cognitive-motor automaticity, and the modified attention allocation index (mAAI). Four hierarchical multiple linear regression models were conducted regressing cDTE and mAAI separately on (1) hypothesis-generated ROIs and (2) exploratory ROIs. For cDTE, cortical thicknesses explained 20.5% (p = 0.014) and 25.9% (p = 0.002) variability in automaticity in the hypothesized ROI and exploratory models, respectively. The dorsal lateral prefrontal cortex (DLPFC) (ß = - 0.479, p = 0.018) and superior parietal cortex (SPC) (ß = 0.467, p = 0.003), and were predictors of automaticity. For mAAI, cortical thicknesses explained 20.7% (p = 0.025) and 28.3% (p = 0.003) variability in attention allocation in the hypothesized ROI and exploratory models, respectively. Thinning of SPC and fusiform gyrus were associated with motor prioritization (ß = - 0.405, p = 0.013 and ß = - 0.632, p = 0.004, respectively), whereas thinning of the DLPFC was associated with cognitive prioritization (ß = 0.523, p = 0.022). Cortical thinning in AD was related to cognitive-motor automaticity and task prioritization, particularly in the DLPFC and SPC. This suggests that these regions may play a primary role in automaticity and attentional strategy during dual-tasking.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

Unique white matter structural connectivity in early-stage drug-naive Parkinson disease.

OBJECTIVE: To investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD). METHODS: We 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. RESULTS: PD-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. CONCLUSION: By 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.

Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters.

Long-term traumatic brain injury due to repeated head impacts (RHI) has been shown to be a risk factor for neurodegenerative disorders, characterized by a loss in cognitive performance. Establishing the correlation between changes in the white matter (WM) structural connectivity measures and neuropsychological test scores might help to identify the neural correlates of the scores that are used in daily clinical setting to investigate deficits due to repeated head blows. Hence, in this study, we utilized high angular diffusion MRI (dMRI) of 69 cognitively impaired and 70 nonimpaired active professional fighters from the Professional Fighters Brain Health Study, and constructed structural connectomes to understand: (a) whether there is a difference in the topological WM organization between cognitively impaired and nonimpaired active professional fighters, and (b) whether graph-theoretical measures exhibit correlations with neuropsychological scores in these groups. A dMRI derived structural connectome was constructed for every participant using brain regions defined in AAL atlas as nodes, and the product of fiber number and average fractional anisotropy of the tracts connecting the nodes as edges. Our study identified a topological WM reorganization due to RHI in fighters prone to cognitive decline that was correlated with neuropsychological scores. Furthermore, graph-theoretical measures were correlated differentially with neuropsychological scores between groups. We also found differentiated WM connectivity involving regions of hippocampus, precuneus, and insula within our cohort of cognitively impaired fighters suggesting that there is a discernible WM topological reorganization in fighters prone to cognitive decline.

Influence of analytic techniques on comparing DTI-derived measurements in early stage Parkinson's disease.

Diffusion tensor imaging (DTI) studies in early Parkinson's disease (PD) to understand pathologic changes in white matter (WM) organization are variable in their findings. Evaluation of different analytic techniques frequently employed to understand the DTI-derived change in WM organization in a multisite, well-characterized, early stage PD cohort should aid the identification of the most robust analytic techniques to be used to investigate WM pathology in this disease, an important unmet need in the field. Thus, region of interest (ROI)-based analysis, voxel-based morphometry (VBM) analysis with varying spatial smoothing, and the two most widely used skeletonwise approaches (tract-based spatial statistics, TBSS, and tensor-based registration, DTI-TK) were evaluated in a DTI dataset of early PD and Healthy Controls (HC) from the Parkinson's Progression Markers Initiative (PPMI) cohort. Statistical tests on the DTI-derived metrics were conducted using a nonparametric approach from this cohort of early PD, after rigorously controlling for motion and signal artifacts during DTI scan which are frequent confounds in this disease population. Both TBSS and DTI-TK revealed a significantly negative correlation of fractional anisotropy (FA) with disease duration. However, only DTI-TK revealed radial diffusivity (RD) to be driving this FA correlation with disease duration. HC had a significantly positive correlation of MD with cumulative DaT score in the right middle-frontal cortex after a minimum smoothing level (at least 13mm) was attained. The present study found that scalar DTI-derived measures such as FA, MD, and RD should be used as imaging biomarkers with caution in early PD as the conclusions derived from them are heavily dependent on the choice of the analysis used. This study further demonstrated DTI-TK may be used to understand changes in DTI-derived measures with disease progression as it was found to be more accurate than TBSS. In addition, no singular region was identified that could explain both disease duration and severity in early PD. The results of this study should help standardize the utilization of DTI-derived measures in PD in an effort to improve comparability across studies and time, and to minimize variability in reported results due to variation in techniques.

Multivariate group-level analysis for task fMRI data with canonical correlation analysis.

Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

Multimodal MR Imaging Signatures of Cognitive Impairment in Active Professional Fighters.

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 × 10-7) 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 × 10-8, 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.

Structural and Functional MRI Differences in Master Sommeliers: A Pilot Study on Expertise in the Brain.

Our experiences, even as adults, shape our brains. Regional differences have been found in experts, with the regions associated with their particular skill-set. Functional differences have also been noted in brain activation patterns in some experts. This study uses multimodal techniques to assess structural and functional patterns that differ between experts and non-experts. Sommeliers are experts in wine and thus in olfaction. We assessed differences in Master Sommeliers' brains, compared with controls, in structure and also in functional response to olfactory and visual judgment tasks. MRI data were analyzed using voxel-based morphometry as well as automated parcellation to assess structural properties, and group differences between tasks were calculated. Results indicate enhanced volume in the right insula and entorhinal cortex, with the cortical thickness of the entorhinal correlating with experience. There were regional activation differences in a large area involving the right olfactory and memory regions, with heightened activation specifically for sommeliers during an olfactory task. Our results indicate that sommeliers' brains show specialization in the expected regions of the olfactory and memory networks, and also in regions important in integration of internal sensory stimuli and external cues. Overall, these differences suggest that specialized expertise and training might result in enhancements in the brain well into adulthood. This is particularly important given the regions involved, which are the first to be impacted by many neurodegenerative diseases.

Effective connectivity during episodic memory retrieval in schizophrenia participants before and after antipsychotic medication.

BACKGROUND: Impairment in episodic memory is one of the most robust findings in schizophrenia. Disruptions of fronto-temporal functional connectivity that could explain some aspects of these deficits have been reported. Recent work has identified abnormal hippocampal function in unmedicated patients with schizophrenia (SZ), such as increased metabolism and glutamate content that are not always seen in medicated SZ. For these reasons, we hypothesized that altered fronto-temporal connectivity might originate from the hippocampus and might be partially restored by antipsychotic medication. METHODS: Granger causality methods were used to evaluate the effective connectivity between frontal and temporal regions in 21 unmedicated SZ and 20 matched healthy controls (HC) during performance of an episodic memory retrieval task. In 16 SZ, effective connectivity between these regions was evaluated before and after 1-week of antipsychotic treatment. RESULTS: In HC, significant effective connectivity originating from the right hippocampus to frontal regions was identified. Compared to HC, unmedicated SZ showed significant altered fronto-temporal effective connectivity, including reduced right hippocampal to right medial frontal connectivity. After 1-week of antipsychotic treatment, connectivity more closely resembled the patterns observed in HC, including increased effective connectivity from the right hippocampus to frontal regions. CONCLUSIONS: 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.

Identification of neural connectivity signatures of autism using machine learning.

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.