Virendra Mishra

Human Fetal Brain Connectome: Structural Network Development from Middle Fetal Stage to Birth

Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage.

Synchronous Changes of Cortical Thickness and Corresponding White Matter Microstructure During Brain Development Accessed by Diffusion MRI Tractography from Parcellated Cortex.

Cortical thickness (CT) changes during normal brain development is associated with complicated cellular and molecular processes including synaptic pruning and apoptosis. In parallel, the microstructural enhancement of developmental white matter (WM) axons with their neuronal bodies in the cerebral cortex has been widely reported with measurements of metrics derived from diffusion tensor imaging (DTI), especially fractional anisotropy (FA). We hypothesized that the changes of CT and microstructural enhancement of corresponding axons are highly interacted during development. DTI and T1-weighted images of 50 healthy children and adolescents between the ages of 7 and 25 years were acquired. With the parcellated cortical gyri transformed from T1-weighted images to DTI space as the tractography seeds, probabilistic tracking was performed to delineate the WM fibers traced from specific parcellated cortical regions. CT was measured at certain cortical regions and FA was measured from the WM fibers traced from same cortical regions. The CT of all frontal cortical gyri, including Brodmann areas 4, 6, 8, 9, 10, 11, 44, 45, 46, and 47, decreased significantly and heterogeneously; concurrently, significant, and heterogeneous increases of FA of WM traced from corresponding regions were found. We further revealed significant correlation between the slopes of the CT decrease and the slopes of corresponding WM FA increase in all frontal cortical gyri, suggesting coherent cortical pruning and corresponding WM microstructural enhancement. Such correlation was not found in cortical regions other than frontal cortex. The molecular and cellular mechanisms of these synchronous changes may be associated with overlapping signaling pathways of axonal guidance, synaptic pruning, neuronal apoptosis, and more prevalent interstitial neurons in the prefrontal cortex. Revealing the coherence of cortical and WM structural changes during development may open a new window for understanding the underlying mechanisms of developing brain circuits and structural abnormality associated with mental disorders.

Brain white matter microstructural alterations in children of type I Gaucher disease characterized with diffusion tensor MR imaging

OBJECTIVESnTo investigate white matter (WM) microstructural alterations in type I Gaucher disease (type I GD) pediatric patients and explore the correlation between the disease duration and WM changes.nnnMETHODSnTwenty-two GD patients and twenty-two sex- and age-matched typical development (TD) children were recruited. Changes in WM were investigated using diffusion tensor imaging (DTI) and applying atlas-based tract analysis. For all DTI measurements, independent-samples t-test was applied to report significant differences between type I GD and TD. Partial correlation was applied to determine whether the disease duration was correlated with DTI measurements.nnnRESULTSnBidirectional fractional anisotropy (FA) changes were found in the bilateral superior cerebellar peduncle, right posterior limb of the internal capsule, right posterior corona radiata, and right posterior thalamic radiation (pu202f<u202f0.05). Higher mean diffusivity (MD)was found in the right superior corona radiata, middle cerebellar peduncle, right posterior thalamic radiation and right superior longitudinal fasciculus (pu202f<u202f0.05) in type I GD. And higher radial diffusivity (RD) was also found in the left superior cerebellar peduncle (pu202f<u202f0.05) in type I GD. The disease duration of type I GD patients is positively correlated with axial diffusivity and MD in multiple major WM tracts.nnnCONCLUSIONnDTI findings supported the microstructural alterations of multiple WM tracts in type I GD patients.

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.

Investigating structural and perfusion deficits due to repeated head trauma in active professional fighters

Repeated head trauma experienced by active professional fighters results in various structural, functional and perfusion damage. However, whether there are common regions of structural and perfusion damage due to fighting and whether these structural and perfusion differences are associated with neuropsychological measurements in active professional fighters is still unknown. To that end, T1-weighted and pseudocontinuous arterial spin labeling MRI on a group of healthy controls and active professional fighters were acquired. Voxelwise group comparisons, in a univariate and multivariate sense, were performed to investigate differences in gray and white matter density (GMD, WMD) and cerebral blood flow (CBF) between the two groups. A significantly positive association between global GMD and WMD was obtained with psychomotor speed and reaction time, respectively, in our cohort of active professional fighters. In addition, regional WMD deficit was observed in a cluster encompassing bilateral pons, hippocampus, and thalamus in fighters (0.49 ± 0.04 arbitrary units (a.u.)) as compared to controls (0.51 ± 0.05a.u.). WMD in the cluster of active fighters was also significantly associated with reaction time. Significantly lower CBF was observed in right inferior temporal lobe with both partial volume corrected (46.9 ± 14.93 ml/100 g/min) and non-partial volume corrected CBF maps (25.91 ± 7.99 ml/100 g/min) in professional fighters, as compared to controls (65.45 ± 22.24 ml/100 g/min and 35.22 ± 12.18 ml/100 g/min respectively). A paradoxical increase in CBF accompanying right cerebellum and fusiform gyrus in the active professional fighters (29.52 ± 13.03 ml/100 g/min) as compared to controls (19.43 ± 12.56 ml/100 g/min) was observed with non-partial volume corrected CBF maps. Multivariate analysis with both structural and perfusion measurements found the same clusters as univariate analysis in addition to a cluster in right precuneus. Both partial volume corrected and non-partial volume corrected CBF of the cluster in the thalamus had a significantly positive association with the number of fights. In addition, GMD of the cluster in right precuneus was significantly associated with psychomotor speed in our cohort of active professional fighters. Our results suggest a heterogeneous pattern of structural and CBF deficits due to repeated head trauma in active professional fighters. This finding indicates that investigating both structural and CBF changes in the same set of participants may help to understand the pathophysiology and progression of cognitive decline due to repeated head trauma.

3D spatially-adaptive canonical correlation analysis: Local and global methods

ABSTRACT Local spatially‐adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially‐adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation‐adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole‐brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low‐pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data. HighlightsThe 2D local CCA method is extended to 3D for fMRI data analysis and solved with an efficient sequential quadratic programming algorithm.A novel 3D global kernel CCA method with spatially‐adaptive filters is proposed.A penalized kernel CCA method is introduced to emphasize the low‐pass spatial filtering property.The kernel CCA methods can produce voxel‐specific activation maps for any contrast matrix of interest.

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease

&NA; The dynamics of the brains intrinsic networks have been recently studied using co‐activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP‐based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP‐related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data‐driven CAP group analysis method is proposed in this study. In the proposed method, a dominant‐CAP (d‐CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d‐CAPs. Alternating d‐CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d‐CAPs, the temporal fraction and spatial consistency of each d‐CAP, and the subject‐specific switching probability among all d‐CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d‐CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground‐truth and computed d‐CAPs was achieved, and detailed comparisons between the proposed method and existing CAP‐based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinsons Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinsons disease (PD) and normal control (NC) groups. Fewer d‐CAPs, skewed distribution of temporal fractions of d‐CAPs, and reduced switching probabilities among final d‐CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d‐CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting‐state fMRI data from subjects with early stage PD. HighlightsA group analysis method is proposed to compute less overlapping dominant co‐activation patterns (d‐CAPs) that represent network dynamics.Detailed comparisons are conducted between the proposed method and existing CAP‐related methods using simulations.Four d‐CAP based measurements are derived to compare network dynamics between different subject groups.Reduced temporal dynamics in most networks are found in the Parkinsons disease group when compared to the normal control group.

MRI CLASSIFIERS CHARACTERIZE MILD TRAUMATIC BRAIN INJURY IN SYMPTOMATIC AND PRESYMPTOMATIC STAGES AND DIFFERENTIATE FROM ALZHEIMER’S DISEASE–RELATED IMPAIRMENT

Figure 1. Voxel based comparison of healthy volunteers separated into young (18 to 36) and older (38 to 71) age groups, compared to unimpaired and impaired retired boxers. Higher CV1 scores reflect greater expression of the pattern of atrophy shown at left. Young and older HV do not differ, Dix U. Meiberth, Xiaochen Hu, Ann-Katrin Schild, Annika Spottke, Frederic Brosseron, Katharina Buerger, Klaus Fliessbach, Michael T. Heneka, Ingo Kilimann, Christoph Laske, Oliver Peters, Josef Priller, Anja Schneider, Stefan J. Teipel, Jens Wiltfang, MichaelWagner, Emrah D€uzel, Frank Jessen and the DELCODE Study Group, Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurology, University of Bonn, Bonn, Germany; Department of Neurodegeneration and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany; Institute for Stroke and Dementia Research (ISD), Klinikum der Universit€at M€unchen, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; University Hospital Bonn, Bonn, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of T€ubingen, T€ubingen, Germany; German Center for Neurodegenerative Diseases (DZNE), T€ubingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charit e-Universitaetsmedizin Berlin, Berlin, Germany; Charit e Universit€atsmedizin Berlin & Berlin Institute of Health, Berlin, Germany; Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany; Department of Psychiatry and Psychotherapy, University Medical Center, Goettingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany. Contact e-mail: dix.meiberth@uk-koeln.de

Advances in functional magnetic resonance imaging data analysis methods using Empirical Mode Decomposition to investigate temporal changes in early Parkinson's disease

Previous neuroimaging studies of Parkinsons disease (PD) patients have shown changes in whole‐brain functional connectivity networks. Whether connectivity changes can be detected in the early stages (first 3 years) of PD by resting‐state functional magnetic resonance imaging (fMRI) remains elusive. Research infrastructure including MRI and analytic capabilities is required to investigate this issue. The National Institutes of Health/National Institute of General Medical Sciences Center for Biomedical Research Excellence awards support infrastructure to advance research goals.

Neuroimaging and neuropsychological assessment of freezing of gait in Parkinson's disease

Freezing of gait (FOG) is a disabling phenomenon characterized by a brief, episodic absence or reduction of forward progression of the feet despite the intention to walk. It is a common cause of falls and mortality in cases with Parkinsons disease (PD). This article reviews neuropsychological and neuroimaging studies to date and introduces a new study of multimodal imaging and cognition in PD‐FOG.