Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph‐theoretical measures ‘local clustering coefficient’ and ‘shortest path length’ derived from resting‐state functional MRI (rs‐fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave‐one‐out cross‐validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs‐fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. Hum Brain Mapp 36:2118–2131, 2015. © 2015 Wiley Periodicals, Inc.     

Information-Theoretic Approach for Automated White Matter Fiber Tracts Reconstruction

Fiber tracking is the most popular technique for creating white matter connectivity maps from diffusion tensor imaging (DTI). This approach requires a seeding process which is challenging because it is not clear how and where the seeds have to be placed. On the other hand, to enhance the interpretation of fiber maps, segmentation and clustering techniques are applied to organize fibers into anatomical structures. In this paper, we propose a new approach to automatically obtain bundles of fibers grouped into anatomical regions. This method applies an information-theoretic split-and-merge algorithm that considers fractional anisotropy and fiber orientation information to automatically segment white matter into volumes of interest (VOIs) of similar FA and eigenvector orientation. For each VOI, a number of planes and seeds is automatically placed in order to create the fiber bundles. The proposed approach avoids the need for the user to define seeding or selection regions. The whole process requires less than a minute and minimal user interaction. The agreement between the automated and manual approaches has been measured for 10 tracts in a DTI brain atlas and found to be almost perfect (kappa >?0.8) and substantial (kappa >?0.6). This method has also been evaluated on real DTI data considering 5?tracts. Agreement was substantial (kappa >?0.6) in most of the cases.     

Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment

BackgroundBrain imaging studies of early Alzheimer's disease (AD) have shown decreased metabolism predominantly in the posterior cingulate cortex (PCC), medial temporal lobe, and inferior parietal lobe. This study investigated functional connectivity between these regions, as well as connectivity between these regions and the whole brain.MethodsFunctional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) studies were performed in subjects with early AD, mild cognitive impairment (MCI), and normal controls.ResultsThe data indicate both decreased fiber connections and disrupted connectivity between the hippocampus and PCC in early AD. The MCI group showed reduced fiber numbers derived from PCC and hippocampus to the whole brain.ConclusionsThe fMRI and DTI results confirmed decreased connectivity from both the PCC and hippocampus to the whole brain in MCI and AD and reduction in connectivity between these two regions, which plausibly represents an early imaging biomarker for AD.     

Individual Functional ROI Optimization Via Maximization of Group-Wise Consistency of Structural and Functional Profiles

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain.     

Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).     

Functionally distinct regions for spatial processing and sensory motor integration in the planum temporale

There has been much debate recently over the functional role played by the planum temporale (PT) within the context of the dorsal auditory processing stream. Some studies indicate that regions in the PT support spatial hearing and other auditory functions, whereas others demonstrate sensory-motor response properties. This multifunctionality has led to the claim that the PT is performing a common computational pattern matching operation, then routing the signals (spatial, object, sensory-motor) into an appropriate processing stream. An alternative possibility is that the PT is functionally subdivided with separate regions supporting various functions. We assess this possibility using a within subject fMRI block design. DTI data were also collected to examine connectivity. There were four auditory conditions: stationary noise, moving noise, listening to pseudowords, and shadowing pseudowords (covert repetition). Contrasting the shadow and listen conditions should activate regions specific to sensory-motor processes, while contrasting the stationary and moving noise conditions should activate regions involved in spatial hearing. Subjects (N = 16) showed greater activation for shadowing in left posterior PT, area Spt, when the shadow and listen conditions were contrasted. The motion vs. stationary noise contrast revealed greater activation in a more medial and anterior portion of left PT. Seeds from these two contrasts were then used to guide the DTI analysis in an examination of connectivity via streamline tractography, which revealed different patterns of connectivity. Findings support a heterogeneous model of the PT, with functionally distinct regions for sensory-motor integration and processes involved in auditory spatial perception.     

Application of neuroanatomical features to tractography clustering

Diffusion tensor imaging allows unprecedented insight into brain neural connectivity in vivo by allowing reconstruction of neuronal tracts via captured patterns of water diffusion in white matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering subsequent data analysis intractable. As a remedy, fiber clustering techniques are able to group fibers into dozens of bundles and thus facilitate analyses. Most existing fiber clustering methods rely on geometrical information of fibers, by viewing them as curves in 3D Euclidean space. The important neuroanatomical aspect of fibers, however, is ignored. In this article, the neuroanatomical information of each fiber is encapsulated in the associativity vector, which functions as the unique “fingerprint” of the fiber. Specifically, each entry in the associativity vector describes the relationship between the fiber and a certain anatomical ROI in a fuzzy manner. The value of the entry approaches 1 if the fiber is spatially related to the ROI at high confidence; on the contrary, the value drops closer to 0. The confidence of the ROI is calculated by diffusing the ROI according to the underlying fibers from tractography. In particular, we have adopted the fast marching method for simulation of ROI diffusion. Using the associativity vectors of fibers, we further model fibers as observations sampled from multivariate Gaussian mixtures in the feature space. To group all fibers into relevant major bundles, an expectation‐maximization clustering approach is employed. Experimental results indicate that our method results in anatomically meaningful bundles that are highly consistent across subjects. Hum Brain Mapp 34:2089–2102, 2013. © 2012 Wiley Periodicals, Inc.     

Quantitative white matter analysis by diffusion tensor imaging and potential functional correlation

Diffusion tensor imaging (DTI) is an MRI modality used to measure the thermal motion of water molecules by combining the measured water diffusion with a simple tensor model of a 3 × 3 symmetric matrix. Since there are many structures that restrict the free motion of water molecules in the brain, we can use the diffusion property of water to study the brain anatomy. Because DTI can provide directional information about axonal fiber bundles, this technique may be one of the most effective MR tools for the investigation of the human white matter anatomy in vivo. Along with the qualitative analysis of fiber pathways using tractography, the quantitative analysis using DTI enables researchers to investigate relationships between white matter anatomy and brain functions as well as to identify tract-specific developmental patterns or disease-specific alterations of the fiber tracts. Several methods have been proposed for whole-brain DTI analysis without an a priori hypothesis. Voxel-based analysis (VBA) is one of the most widely used approaches, although it has concerning limitations, especially when isotropic spatial smoothing is applied. Alternative methods such as tract-based spatial statistics and atlas-based analysis have been introduced to overcome the limitations of VBA. Future studies combining the anatomical connectivity illustrated by using DTI and the functional connectivity illustrated by using resting-state fMRI will provide an emerging landscape of human brain connectivity.     

Altered Functional and Anatomical Connectivity in Schizophrenia

Background: Schizophrenia is characterized by a lack of integration between thought, emotion, and behavior. A disruption in the connectivity between brain processes may underlie this schism. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were used to evaluate functional and anatomical brain connectivity in schizophrenia. Methods: In all, 29 chronic schizophrenia patients (11 females, age: mean = 41.3, SD = 9.28) and 29 controls (11 females, age: mean = 41.1, SD = 10.6) were recruited. Schizophrenia patients were assessed for severity of negative and positive symptoms and general cognitive abilities of attention/concentration and memory. Participants underwent a resting-fMRI scan and a DTI scan. For fMRI data, a hybrid independent components analysis was used to extract the group default mode network (DMN) and accompanying time-courses. Voxel-wise whole-brain multiple regressions with corresponding DMN time-courses was conducted for each subject. A t-test was conducted on resulting DMN correlation maps to look between-group differences. For DTI data, voxel-wise statistical analysis of the fractional anisotropy data was carried out to look for between-group differences. Voxel-wise correlations were conducted to investigate the relationship between brain connectivity and behavioral measures. Results: Results revealed altered functional and anatomical connectivity in medial frontal and anterior cingulate gyri of schizophrenia patients. In addition, frontal connectivity in schizophrenia patients was positively associated with symptoms as well as with general cognitive ability measures. Discussion: The present study shows convergent fMRI and DTI findings that are consistent with the disconnection hypothesis in schizophrenia, particularly in medial frontal regions, while adding some insight of the relationship between brain disconnectivity and behavior.     

Morphofunctional organization in three patients with unilateral polymicrogyria: combined use of diffusion tensor imaging and functional magnetic resonance imaging

We examined the fiber organization of the brain in three patients with unilateral polymicrogyria (PMG) using diffusion tensor imaging (DTI) in combination with functional magnetic resonance imaging (fMRI). DTI revealed altered fiber tract architecture in patients with PMG. Long projection fibers, such as the corticospinal tract, were reduced the most, whereas long association fibers were less affected. The diminution of the fiber tracts was relevant to the loss of functionality of the PMG-affected cortex. Our preliminary study suggests that the combination of DTI and fMRI reinforces the clinical assessment of functionality in PMG.     

Modulating functional connectivity between medial frontopolar cortex and amygdala by inhibitory and excitatory transcranial magnetic stimulation

The prefrontal‐limbic network in the human brain plays a major role in social cognition, especially cognitive control of emotion. The medial frontopolar cortex (mFP; Brodmann Area 10) and the amygdala are part of this network and display correlated neuronal activity in time, as measured by functional magnetic resonance imaging (fMRI). This functional connectivity is dynamic, sensitive to training, and affected in mental disorders. However, the effects of neurostimulation on functional connectivity within this network have not yet been systematically investigated. Here, we investigate the effects of both low‐ and high‐frequency repetitive transcranial magnetic stimulation (rTMS) to the right mFP on functional connectivity between mFP and amygdala, as measured with resting state fMRI (rsfMRI). Three groups of healthy participants received either low‐frequency rTMS (1 Hz; N = 18), sham TMS (1 Hz, subthreshold; N = 18) or high‐frequency rTMS (20 Hz; N = 19). rsfMRI was acquired before and after (separate days). We hypothesized a modulation of functional connectivity in opposite directions compared to sham TMS through adjustment of the stimulation frequency. Groups differed in functional connectivity between mFP and amygdala after stimulation compared to before stimulation (low‐frequency: decrease, high‐frequency: increase). Motion or induced changes in neuronal activity were excluded as confounders. Results show that rTMS is effective for increasing and decreasing functional coherence between prefrontal and limbic regions. This finding is relevant for social and affective neuroscience as well as novel treatment approaches in psychiatry.     

Regional networks underlying interhemispheric connectivity: an EEG and DTI study in healthy ageing and amnestic mild cognitive impairment

Interhemispheric coherence derived from electroencephalogram (EEG) recordings is a measure of functional interhemispheric connectivity. Diffusion tensor imaging (DTI) determines the integrity of subcortical fiber tracts. We studied the pattern of subcortical fiber tracts underlying interhemispheric coherence and its alteration in 16 subjects with amnestic mild cognitive impairment (MCI), an at risk syndrome for Alzheimer's disease, and 20 cognitively healthy elderly control subjects using resting state EEG and high resolution DTI at 3 T. We used a multivariate network approach based on principal component analysis to determine effects of coherence on the regional pattern of diffusivity. Temporo-parietal coherence in the alpha band was significantly correlated with diffusivity in predominantly posterior white matter tracts including posterior corpus callosum, parietal, temporal and occipital lobe white matter, thalamus, midbrain, pons, and cerebellum, both in MCI subjects and controls (P < 0.05). In MCI subjects, frontal coherence in the alpha band was significantly correlated with a predominately frontal pattern of diffusivity including fiber tracts of the anterior corpus callosum, frontal lobe white matter, thalamus, pons, and cerebellum (P < 0.05). The study provides a methodology to access specific networks of subcortical fiber tracts subserving the maintenance of interhemispheric resting state coherence in the human brain.     

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.     

Tract-based Analysis of Magnetization Transfer Ratio and Diffusion Tensor Imaging of the Frontal and Frontotemporal Connections in Schizophrenia

Background: In the pathophysiology of schizophrenia, aberrant connectivity between brain regions may be a central feature. Diffusion tensor imaging (DTI) studies have shown altered fractional anisotropy (FA) in white brain matter in schizophrenia. Focal reductions in myelin have been suggested in patients using magnetization transfer ratio (MTR) imaging but to what extent schizophrenia may be related to changes in MTR measured along entire fiber bundles is still unknown. Methods: DTI and MTR images were acquired with a 1.5-T scanner in 40 schizophrenia patients and compared with those of 40 healthy participants. The mean FA and mean MTR were measured along the genu of the corpus callosum and the left and right uncinate fasciculus. Results: A higher mean MTR of 1% was found in the right uncinate fasciculus in patients compared with healthy participants. A significant negative correlation between age and mean FA in the left uncinate fasciculus was found in schizophrenia patients but not in healthy participants. Conclusions: Decreased FA in the left uncinate fasciculus may be more prominent in patients with longer illness duration. The increased mean MTR in the right uncinate fasciculus could reflect a compensatory role for myelin in these fibers or possibly represent aberrant frontotemporal connectivity.     

White matter fiber tractography and color mapping of the normal human cerebellum with diffusion tensor imaging

Diffusion tensor imaging (DTI) color mapping and fiber tractography was used to study the white matter within the cerebellum along with the afferent and efferent tracts associated with the cerebellum in 24 normal human subjects. The most prominent structures that can be readily identified using these DTI techniques are the middle, inferior and superior cerebellar peduncles. Furthermore DTI shows transverse white matter fiber that cross between the two cerebellar hemispheres at the level of the vermis. At the hemispheric level fibers to the dentate, to the emboliform nuclei are clearly visible on DTI as is the afferent pathway represented by the middle cerebellar peduncle. Selective DTI fiber tractography provides very exquisite images of the cerebellar peduncles and of the fibers projecting to and from the cerebellar cortex. This study demonstrates that DTI is complementary to conventional MRI in that DTI elucidates the orientation of white matter fiber bundles that are associated with the cerebellum. Therefore we anticipate that DTI will become an important adjunct to conventional MRI for clinical and basic studies of cerebellar ataxias and congenital disorders involving the cerebellum and brain stem. This work provides a summary of the normal DTI appearance of the cerebellar white matter which will be useful for interpreting DTI results in clinical populations.     

Diffusion tensor imaging and fiber tractography in acute stroke

Diffusion tensor imaging (DTI) permits the quantitative evaluation of white matter pathology using measures of diffusion anisotropy. Fiber tractography based on DTI can reveal the three-dimensional white matter connectivity of the human brain. DTI fiber tractography is used to localize stroke lesions in relation to functionally important pathways and to assess wallerian degeneration, which may allow more accurate prognosis of long-term recovery or disability. DTI also improves the evaluation of hypoxic-ischemic injury to the developing brain of newborns and infants. DTI and fiber tractography may prove useful in elucidating alterations in brain connectivity resulting from neuroplasticity after stroke.     

The default mode network and cognition in Parkinson's disease: A multimodal resting‐state network approach

Involvement of the default mode network (DMN) in cognitive symptoms of Parkinson''s disease (PD) has been reported by resting‐state functional MRI (rsfMRI) studies. However, the relation to metabolic measures obtained by [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET) is largely unknown. We applied multimodal resting‐state network analysis to clarify the association between intrinsic metabolic and functional connectivity abnormalities within the DMN and their significance for cognitive symptoms in PD. PD patients were classified into normal cognition (n = 36) and mild cognitive impairment (MCI; n = 12). The DMN was identified by applying an independent component analysis to FDG‐PET and rsfMRI data of a matched subset (16 controls and 16 PD patients) of the total cohort. Besides metabolic activity, metabolic and functional connectivity within the DMN were compared between the patients'' groups and healthy controls (n = 16). Glucose metabolism was significantly reduced in all DMN nodes in both patient groups compared to controls, with the lowest uptake in PD‐MCI (p < .05). Increased metabolic and functional connectivity along fronto‐parietal connections was identified in PD‐MCI patients compared to controls and unimpaired patients. Functional connectivity negatively correlated with cognitive composite z‐scores in patients (r = −.43, p = .005). The current study clarifies the commonalities of metabolic and hemodynamic measures of brain network activity and their individual significance for cognitive symptoms in PD, highlighting the added value of multimodal resting‐state network approaches for identifying prospective biomarkers.