DTI Data Research Articles

Brain-wide structural connectivity alterations under the control of Alzheimer risk genes.

Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown. We used the genotype and DTI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructing the brain network for each subject, we extracted three types of link measures, including fiber anisotropy, fiber length and density. We then performed a targeted genetic association analysis of brain-wide connectivity measures using general linear regression models. Age at scan and gender were included in the regression model as covariates. For fair comparison of the genetic effect on different measures, fiber anisotropy, fiber length and density were all normalized with mean as 0 and standard deviation as one.We aim to discover the abnormal brain-wide network alterations under the control of 34 AD risk SNPs identified in previous large-scale genome-wide association studies. After enforcing the stringent Bonferroni correction, rs10498633 in SLC24A4 were found to significantly associated with anisotropy, total number and length of fibers, including some connecting brain hemispheres. With a lower level of significance at 5e-6, we observed significant genetic effect of SNPs in APOE, ABCA7, EPHA1 and CASS4 on various brain connectivity measures.

In vivo histopathological staging in C9orf72-associated ALS: A tract of interest DTI study

BackgroundDiffusion tensor imaging (DTI) can identify amyotrophic lateral sclerosis (ALS)-associated patterns of brain alterations at the group level according to a neuropathological staging system. ObjectiveThe study was designed to investigate the in vivo staging in ALS patients with the C9orf72 expansion and potential differences to ALS patients with the SOD1 mutation. MethodsDTI-based white matter mapping was performed both by an unbiased voxel-wise statistical comparison and by a hypothesis-guided tract-wise analysis of fractional anisotropy (FA) maps according to the ALS-staging pattern for 27 ALS patients with C9orf72 expansion vs 15 ALS patients with SOD1 mutation vs 32 matched healthy controls. Clinical and neuropsychological data were acquired and correlated to DTI data. ResultsThe analysis of white matter integrity demonstrated regional FA reductions along the CST and also in frontal and prefrontal brain areas according to the proposed propagation pattern for the ALS patients with C9orf72 expansion and sporadic patients. This pattern could not be identified for the SOD1 mutation at the group level. In contrast, in the tract-specific analysis according to the neuropathological ALS-staging pattern, C9orf72 expansion ALS patients showed significant alterations of ALS-related tract systems similar to sporadic patients. ConclusionsThe DTI study including the tract-of-interest-based analysis showed a microstructural corticoefferent involvement pattern according to the staging scheme in C9orf72-associated ALS patients but not in the SOD1 mutation.

Changes of brain DTI in healthy human subjects after 520 days isolation and confinement on a simulated mission to Mars

IntroductionLong-term confinement is known to be a stressful experience with multiple psycho-physiological effects. In the MARS500 project, a real-time simulation of a space-flight to Mars conducted in a hermetically isolated habitat, effects of long-term confinement could be investigated in a unique manner. The aim of this study was to evaluate effects of long-term-confinement on brain cytoarchitecture. Material & MethodsThe participants of the MARS500 project underwent 3T-MR imaging including a dedicated DTI-sequence before the isolation, right after ending of confinement and 6 months after the experiment. Voxelwise statistical analysis of the DTI data was carried out using tract-based-spatial statistics, comparing an age-matched control group. ResultsAt all three sessions, significant lower fractional anisotropy (FA) than in controls was found in the anterior parts of the callosal body of the participants. Furthermore, after ending of confinement a wide-spread FA reduction could be seen in the right hemisphere culminating in the temporo-parietal-junction-zone. All these areas with decreased FA predominantly showed an elevated radial diffusivity and mean diffusivity while axial diffusivity was less correlated. DiscussionLong-term confinement does have measurable effects on the microstructure of the brain white matter. We assume effects of sensory deprivation to account for the regional FA reductions seen in the right TPJ. The differences in the Corpus callosum were interpreted as due to preliminary conditions, e.g. personality traits or training effects. FA and radial diffusivity were the predominant DTI parameters with significant changes, suggesting underlying processes of myelin plasticity.

The role of diffusion tensor imaging and tractography for deep brain stimulation planning in treatment of obsessive-compulsive disorder

Introduction: Obsessive-compulsive disorder (OCD) is a common, chronic and long-lasting mental disorder. The current first-line therapy for OCD is high doses of selective serotonin reuptake inhibitor (SSRI) and cognitive-behavioral psychotherapy. For patients with refractory symptoms, studies demonstrated that they may respond well to Deep Brain Stimulation (DBS), a technique that can modulate altered neuronal activity. Some stimulation sites are currently being used as targets to DBS and showed good response, but further analyses are necessary to improve the location of the electrodes since some patients demonstrated a poor outcome. Neuroimaging that assess white matter such as diffusion tensor image and tractography can evaluate the quality of the targets and assess the tracts that are affected by the electric field of the electrodes used in the surgery. Our hypothesis is that the patient outcome depends on the trajectory of the tracts that are affected by the electric field.Objectives: Our aim is to investigate which tracts connected with the stimulation sites contribute to clinical improvement effects and weather is possible to predict the outcomes based on connectivity.Methodology: We analyzed 4 patients (2 female) with treatment-refractory obsessive-compulsive disorder undergoing deep brain stimulation targeting the anterior limb of the internal capsule/ nucleus accumbens (ALIC). We will perform tractography analysis of the fibers using the volume of active tissue (VTA) as the region of interest. We will relate the alteration in OCD symptom severity on Yale-Brown obsessive-compulsive scale (Y-BOCS) between the condition before surgery and one-year follow-up with the tracts involved with the stimulation sites using DTI data such as fractal anisotropy and mean diffusivity.Partial Results: Half of the patients did show great improvement and the other two maintained a bad outcome. We found that active stimulation of the ALIC more lateral and posterior of the nucleus was associated with a better outcome. Currently, the description of the tracts involved in patients with better outcomes is performed. The tracts involved with this result will be determined by analysis of DTI and tractography.Discussion and Conclusion: For a future perspective, the results will be important to improve the stimulation sites in DBS surgery resulting in better outcomes.

Human visual processing for spatial 3-D imaging recognition: Cortical connectivity deficits in Parkinson's spatial perceptual visual pathway

Introduction Parkinson's disease (PD) is a Neurodegenerative disorder traditionally linked to motor impairment but recent findings have revealed, patients suffering from PD exhibit decline in visual-spatial skills [1] [2]. The aim of this study is to explore, neural structural connectivity between the superior parietal lobe (BA 5) and the primary visual cortex (BA 17, 18 & 19) in PD patients. Generally, this pathway modulates visual and spatial domains, hence if found to be impaired in PD patients could explain the dysfunction in visual-spatial skills. Methods In this research, we attempted to correlate the dorsal stream visual pathway in Parkinson's Patients. The study was carried out through ;“Diffusion Imaging Fibre Tractography;” which involves 120 DTI data sets from control and PD patients between 60 and 120 years with IRB approval. Observations It was observed that fibers in the females of the control group were higher (Fig 1) when compared to that of the males (Fig 2), and on completion, males displayed more prominent changes in numbers and volumes of where stream pathway;”(Fig 3). In males both right and left hemisphere were affected, whereas with female much difference is not noticed. The results are statistically significant at P Results Based on our observations, destruction in the where stream pathway were identified, and is believed as an underlying cause for deficiency in visual spatial recognition. However, for better understanding of this findings, functional and effective connectivity analysis must be recommended.

Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases.

Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.

Pediatric Anoxic Brain Injury - A Possible Locked-In Syndrome

Abstract INTRODUCTION Although drowning is a leading cause of death and neurological morbidity in young children, clinical management and prognostication following injury is poor. The most devastating disability from drowning results from anoxic brain injury (ABI). Standard-of-care imaging and assessment methods have proven inadequate in the evaluation of brain damage and prediction of functional sequelae, and thus, have had minimal clinical impact in these patients and other patients with disorders of consciousness. METHODS A total of 11 children with drowning-related ABI and 11 age- and gender-matched neurotypical controls (4-12 yr) were scanned during mildly sedated sleep; T1-weighted, DTI, and resting-state fMRI blood oxygen level dependent (BOLD) data were acquired. Voxel-based morphometry (VBM) was implemented on T1 data, tract-based spatial statistics (TBSS) was implemented on DTI data, and independent components analysis (ICA) was implemented at group and per-subject levels on fMRI data to investigate the integrity of resting-state networks (RSNs). Extensive functional evaluations were conducted and a systematic behavioral evaluation form and scoring system were developed to correlate imaging and behavioral measures. RESULTS VBM investigations of grey and white matter respectively revealed predominant central subcortical pathology in the basal ganglia and posterior limbs of the internal capsule (PLIC). White matter microstructural investigation with TBSS of DTI data revealed focal damage in bilateral PLICs. ICA of resting-state fMRI data revealed principal impairment of motor-related cortical networks (basal ganglia, cerebellum) and striking preservation of networks involved in perception (visual, auditory, sensorimotor), cognition, and emotion. These findings closely agreed with neurobehavioral assessments. CONCLUSION Together, our observations suggest that motor deficits underlie the inability to communicate and convey relatively intact cognitive, perceptual, and emotive capabilities in pediatric post-drowning ABI, depicting a locked-in-type syndrome. Several prognostic, therapeutic, and ethical correlates follow. Furthermore, the developed imaging protocol is suitable for clinical use and highly applicable to other patient populations and disorders of consciousness.

Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI.

Image-based classification of lower-grade glioma molecular subtypes has substantial prognostic value. Diffusion tensor imaging has shown promise in lower-grade glioma subtyping but currently requires lengthy, nonstandard acquisitions. Our goal was to investigate lower-grade glioma classification using a machine learning technique that estimates fractional anisotropy from accelerated diffusion MR imaging scans containing only 3 diffusion-encoding directions. Patients with lower-grade gliomas (n = 41) (World Health Organization grades II and III) with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status were imaged preoperatively with DTI. Whole-tumor volumes were autodelineated using conventional anatomic MR imaging sequences. In addition to conventional ADC and fractional anisotropy reconstructions, fractional anisotropy estimates were computed from 3-direction DTI subsets using DiffNet, a neural network that directly computes fractional anisotropy from raw DTI data. Differences in whole-tumor ADC, fractional anisotropy, and estimated fractional anisotropy were assessed between IDH-wild-type and IDH-mutant lower-grade gliomas with and without 1p/19q codeletion. Multivariate classification models were developed using whole-tumor histogram and texture features from ADC, ADC + fractional anisotropy, and ADC + estimated fractional anisotropy to identify the added value provided by fractional anisotropy and estimated fractional anisotropy. ADC (P = .008), fractional anisotropy (P < .001), and estimated fractional anisotropy (P < .001) significantly differed between IDH-wild-type and IDH-mutant lower-grade gliomas. ADC (P < .001) significantly differed between IDH-mutant gliomas with and without codeletion. ADC-only multivariate classification predicted IDH mutation status with an area under the curve of 0.81 and codeletion status with an area under the curve of 0.83. Performance improved to area under the curve = 0.90/0.94 for the ADC + fractional anisotropy classification and to area under the curve = 0.89/0.89 for the ADC + estimated fractional anisotropy classification. Fractional anisotropy estimates made from accelerated 3-direction DTI scans add value in classifying lower-grade glioma molecular status.

FV 36 Cognitive deficits and microstructural correlates in Parkinsons disease: DTI data from the LANDSCAPE study

FV 36 Cognitive deficits and microstructural correlates in Parkinsons disease: DTI data from the LANDSCAPE study

Retrospective distortion correction of diffusion tensor imaging data by semi-elastic image fusion – Evaluation by means of anatomical landmarks

ObjectiveDiffusion tensor imaging (DTI) based on echo-planar imaging (EPI) can suffer from geometric image distortions in comparison to conventional anatomical magnetic resonance imaging (MRI). Therefore, DTI-derived information, such as fiber tractography (FT) used for treatment planning of brain tumors, might be associated with spatial inaccuracies when linearly projected on anatomical MRI. Hence, a non-linear, semi-elastic image fusion shall be evaluated in this study that aims at correcting for image distortions in DTI. Patients and methodsIn a sample of 27 patient datasets, 614 anatomical landmark pairs were retrospectively defined in DTI and T1- or T2-weighted three-dimensional (3D) MRI data. The datasets were processed by a commercial software package (Elements Image Fusion .0; Brainlab AG, Munich, Germany) providing rigid and semi-elastic fusion functionalities, such as DTI distortion correction. To quantify the displacement prior to and after semi-elastic fusion, the Euclidian distances of rigidly and elastically fused landmarks were evaluated by means of descriptive statistics and Bland-Altman plot. ResultsFor rigid and semi-elastic fusion mean target registration errors of 3.03 ± 2.29 mm and 2.04 ± 1.95 mm were found, respectively, with 91% of the evaluated landmarks moving closer to their position determined in T1- or T2-weighted 3D MRI data after distortion correction. Most efficient correction was achieved for non-superficial landmarks showing distortions up to 1 cm. ConclusionThis study indicates that semi-elastic image fusion can be used for retrospective distortion correction of DTI data acquired for image guidance, such as DTI FT as used for a broad range of clinical indications.

Discovering hierarchical common brain networks via multimodal deep belief network.

Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g., fMRI and DTI. However, deep learning models have been rarely used to explore the relation between brain structure and function yet. In this paper, we proposed a novel multimodal deep believe network (DBN) model to discover and quantitatively represent the hierarchical organizations of common and consistent brain networks from both fMRI and DTI data. A prominent characteristic of DBN is that it is capable of extracting meaningful features from complex neuroimaging data with a hierarchical manner. With our proposed DBN model, three hierarchical layers with hundreds of common and consistent brain networks across individual brains are successfully constructed through learning a large dimension of representative features from fMRI/DTI data.

Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint.

Diffusion tensor magnetic resonance imaging (DT-MRI, or DTI) is a promising technique for invasively probing biological tissue structures. However, DTI is known to suffer from much longer acquisition time with respect to conventional MRI and the problem is worsened when dealing with in vivo acquisitions. Therefore, faster DTI for both ex vivo and in vivo scans is highly desired. This paper proposes a new compressed sensing (CS) reconstruction method that employs local low-rank (LLR) model and three-dimensional (3D) total variation (TV) constraint to reconstruct cardiac diffusion-weighted (DW) images from highly undersampled k-space data. The LLR model takes the set of DW images corresponding to different diffusion gradient directions as a 3D image volume and decomposes the latter into overlapping 3D blocks. Then, the 3D blocks are stacked as two-dimensional (2D) matrix. Finally, low-rank property is applied to each block matrix and the 3D TV constraint to the 3D image volume. The underlying constrained optimization problem is finally solved using the first-order fast method. The proposed method is evaluated on real ex vivo cardiac DTI data as a prerequisite to in vivo cardiac DTI applications. The results on real human ex vivo cardiac DTI images demonstrate that the proposed method exhibits lower reconstruction errors for DTI indices, including fractional anisotropy (FA), mean diffusivities (MD), transverse angle (TA), and helix angle (HA), compared to existing CS-based DTI image reconstruction techniques. The proposed method provides better reconstruction quality and more accurate DTI indices in comparison with the state-of-the-art CS-based DW image reconstruction methods.

Motor skill learning induces brain network plasticity: A diffusion-tensor imaging study

Motor skills and the acquisition of brain plasticity are important topics in current research. The development of non-invasive white matter imaging technology, such as diffusion-tensor imaging and the introduction of graph theory make it possible to study the effects of learning skills on the connection patterns of brain networks. However, few studies have characterized the brain network topological features of motor skill learning, especially open skill. Given the need to interact with environmental changes in real time, we hypothesized that the brain network of high-level open-skilled athletes had higher transmission efficiency and stronger interaction in attention, visual and sensorimotor networks. We selected 21 high-level basketball players and 25 ordinary individuals as control subjects, collected their DTI data, built a network of brain structures, and used graph theory to analyze and compare the network properties of the two groups at global and regional levels. In addition, we conducted a correlation analysis on the training years of high-level athletes and brain network nodal parameters on the regional level to assess the relationship between brain network topological characteristics and skills learning. We found that on the global-level, the brain network of high-level basketball players had a shorter path length, small-worldness, and higher global efficiency. On the regional level, the brain nodes of the high-level athletes had nodal parameters that were significantly higher than those of control groups, and were mainly distributed in the visual network, the default mode network, and the attention network. The changes in brain node parameters were significantly related to the number of training years.

Prognostic Value of Craniovertebral Junction Diffusion Tensor Imaging in Patients with Chiari Type 1 Malformation.

To examine the spinal cord status by using diffusion tensor imagıng (DTI) and tractography preoperatively and postoperatively in patients with Chiari malformation type 1 (CM1) and compare the results with the data obtained from healthy people. Overall, 48 patients (33 patients with CM1, and 15 in the control group for DTI and tractography measurements) were included in the study. Mean age was 37.7 ± 11.8 years (minimum and maximum: 12 and 57 years). The DTI and tractography data were obtained from the craniocervical region in patients with CM1 and control group. Patients with CM1 were operated using the suboccipital decompression technique without opening the dura. Surgical results were evaluated using Chicago Chiari Outcome Scale (CCOS) and Asgari scale. Based on the CCOS and Asgari scale results, the surgical technique was determined to be clinically beneficial. The DTI and tractography values from the pontobulbar, cervicomedullary, and spinal cord C3â€"4 levels in patients with CM1 were compared to those of the control group. These values were observed to be near normal after surgery in patients with CM1. Based on the improvement in DTI-tractography data observed in our study, DTI and tractography can serve as a guiding measurement method for assessing the prognosis of patients with CM1.

The effects of variations in tissue microstructure from postmortem rat brain on the asymmetry of the water proton resonance.

This work was performed to investigate the effects of tissue microstructure from postmortem rat brain on the shape of the water proton spectrum. Perfusion-fixed, resected rat brains (N = 4) were imaged at 9.4T. 3D DTI and 3D echo-planar spectroscopic imaging (EPSI) data were acquired with 150 μm isotropic resolution. DTI data were acquired over 60 directions with b = 3000 s/mm2 . Water spectra were produced from EPSI data acquired over 128 echoes, with 2.9 Hz spectral resolution. A voxel-wise metric reflecting spectral asymmetry about the peak of the resonance was computed and compared with orientation estimates from DTI data by fitting data with the susceptibility anisotropy model. Asymmetric broadening of the water resonance was computed for mixed populations of grey and/or white matter as determined by thresholding the fractional anisotropy. Asymmetry was shown to be differentially affected by tract orientation relative to B0 in high FA voxels, whereas low FA voxels exhibited little sensitivity. Anatomic structures in the hippocampus were also found to produce distinct changes in the water resonance. Present results demonstrate that structural variations in tissue architecture cause characteristic, reproducible changes in the water resonance shape. This suggests that water spectra are sensitive to cytoarchitectural variations in brain tissue.

Radial diffusivity as an imaging biomarker for early diagnosis of non-demented amyotrophic lateral sclerosis.

To explore the sensitivity of potential DTI-based biomarkers in detecting microstructural changes for whole-brain white matter in early stage amyotrophic lateral sclerosis (ALS), analyze the relationship between the DTI indices and disease status, and further clarify potential brain regions for disease monitoring and clinical assessment. Thirty-three non-demented ALS patients and 32 age- and gender-matched subjects participated in this study. DTI data were acquired via 3.0T MRI scanner. Maps of diffusion-related indices including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were obtained. Tract-based spatial statistics (TBSS) were used to investigate whole-brain white matter changes of each index. Correlation analyses between both brain-wide and volume-of-interest (VOI)-wide white matter alterations and clinical factors including ALSFRS-R scores, disease duration, and progression rate were performed. Compared to healthy subjects, ALS patients showed significantly increased RD, MD and reduced FA, mainly along the corticospinal tract (CST) and the body of corpus callosum (CC). Increases in RD were broader than decreases in FA, in CST of both hemispheres. Meanwhile, involvement of several extra-motor regions was also revealed by RD. Significant positive correlation between ALSFRS-R scores and FA, negative correlation between ALSFRS-R and RD were found in left CST. RD may be the most sensitive biomarker for the detection of early demyelination of white matter. Both RD and FA may serve as objective biomarkers for disease severity assessment. CST may be the most affected brain region in non-demented ALS. • Changes in RD were broader than those in FA in bilateral CST. • Involvement of extra-motor regions was uncovered by RD. • FA and RD in CST were related to ALSFRS-R scores.

Age Related Changes in Topological Properties of Brain Functional Network and Structural Connectivity

Introduction: There are still uncertainties about the true nature of age related changes in topological properties of the brain functional network and its structural connectivity during various developmental stages. In this cross- sectional study, we investigated the effects of age and its relationship with regional nodal properties of the functional brain network and white matter integrity.Method: DTI and fMRI data were acquired from 458 healthy Chinese participants ranging from age 8 to 81 years. Tractography was conducted on the DTI data using FSL. Graph Theory analyses were conducted on the functional data yielding topological properties of the functional network using SPM and GRETNA toolbox. Two multiple regressions were performed to investigate the effects of age on nodal topological properties of the functional brain network and white matter integrity.Result: For the functional studies, we observed that regional nodal characteristics such as node betweenness were decreased while node degree and node efficiency was increased in relation to increasing age. Perversely, we observed that the relationship between nodal topological properties and fasciculus structures were primarily positive for nodal betweenness but negative for nodal degree and nodal efficiency. Decrease in functional nodal betweenness was primarily located in superior frontal lobe, right occipital lobe and the global hubs. These brain regions also had both direct and indirect anatomical relationships with the 14 fiber bundles. A linear age related decreases in the Fractional anisotropy (FA) value was found in the callosum forceps minor.Conclusion: These results suggests that age related differences were more pronounced in the functional than in structural measure indicating these measures do not have direct one-to-one mapping. Our study also indicates that the fiber bundles with longer fibers exhibited a more pronounced effect on the properties of functional network.

T132. Connectivity analysis of glioma patients with language disturbances

Language production involves a complex brain network that should be taken into account in pre-surgical planning of patients with brain tumors. Connectivity analysis (functional and structural) of magnetic resonance imaging data (cMRI) enables the study of the functional interaction and structural relationship between brain regions. Our goal was to correlate cMRI data with clinical manifestations of language disturbances We recruited sixteen patients (mean age of 47 years old; 8 female) diagnosed with low and high grade brain gliomas (47%) located in frontal (57%), temporal (17%), parietal (13%) lobes and in the insular region (13%). They performed pre-operative clinical language tests, structural (diffusion tensor imaging – DTI), and functional MRI (fMRI) using a battery of language paradigms. We extracted connectivity metrics (i.e. node degree, betweeness centrality, partition coefficient, and clustering coefficient) from thirteen brain regions and normalized the data (i.e. using mean and standard deviation) within each region and across patients. For a set of brain regions involved in language processing (i.e. opercular, supramarginal and superior temporal gyrus), we evaluated the correlation between local connectivity metrics and brain tumor location, language clinical tests and the outcome of cortical stimulation during awake craniotomy (i.e. whether it produced language disturbances). We analyzed seventeen MRI datasets (i.e. one patient received treatment twice and three others had previously performed craniotomies) including DTI data in only twelve patients. Clinically, three presented conduction aphasia (two with temporal and one with a frontal lobe tumor), four had anomic aphasia (two with frontal, one with insular and another with temporal lobe tumor) and two with expressive aphasia with frontal lobe tumors. The extracted metrics failed to correlate with tumor location. Language tests correlated with higher partition (r = 0.75) coefficient of opercular or supramarginal gyrus. Cortical stimulation at opercular or supramarginal gyrus correlated either with a higher clustering (r = 0.68) or lower partition (r = −0.64) coefficient in supramarginal or opercular gyri, respectively. cMRI from DTI and task-based language paradigms correlates with clinical evaluation language tests as expected. Also, connectivity measures suggest a homo-lateral compensation in the language network when opercular or supramarginal gyri are affected besides contralateral as reported in literature.

Reduced White Matter Integrity With Cognitive Impairments in End Stage Renal Disease.

Background: End-stage renal disease (ESRD) is a serious public health problem, which can often lead to multiorgan dysfunction, such as cerebrovascular disease and cognitive damage. It is essential to understand cognitive impairment in patients with ESRD to develop better ESRD treatment and prevent further cognitive impairment. Cognitive impairment is believed to be related to structural abnormalities in the brain.Purpose: To investigate white matter microstructural abnormalities in patients with ESRD using TBSS analysis of DTI and to explore the possible mechanisms underlying the impaired cognitive function.Materials and Methods: A TBSS analysis of DTI data was to investigate the microstructural changes in their WM over the whole brain. We chose the white matter tracts or regions with significantly reduced FA as the regions of interest (ROIs), Pearson's correlations were performed between clinical indicators (Mini-Mental State Examination (MMSE), digit span task scores, serum creatinine, blood urea nitrogen and hemodialysis duration) and the mean FA value of the ROIs in the ESRD patients.Results: Lower FA and higher MD, AD and RD values were observed in widespread and symmetrical WM in ESRD patients than healthy controls (HCs), Pearson correlation analysis revealed a significantly positive correlation between the Mini-Mental State Examination (MMSE) scores and FA values in the right corona radiata and left anterior thalamic radiation (ATR) and demonstrated a significantly negative correlation between FA values and the serum creatinine and blood urea nitrogen in the ATR (P < 0.01) in addition, digit span task scores positively correlate with the FA value in the left anterior rather than in the corona radiata. No cluster survived when we adopted the False Discovery Rate (FDR) correction to multiple comparisons.Conclusion: Our study indicate widespread impairment of the white matter in ESRD patients. Damage to the thalamic radiation and corona radiata may affect cognitive function in ESRD patients, the reduced integrity of ATR may tend to affect the working memory while the damage to the corona radiata may involve the executive function impaired in ESRD patients. The accumulation of serum creatinine and blood urea nitrogen may contribute to the WM impairment.

O1. Classification of Patients With Bipolar Disorder Based on DTI Data: Relationship With Clinical Dimensions

There is growing evidence for the potential of machine learning techniques for brain-based diagnosis of psychiatric disorders. However, few studies have been conducted to predict the diagnosis of bipolar disorder (BD) based on structural connectivity. This study aims at using machine learning algorithms to identify biomarkers that can be used to classify BD patients from healthy controls (HC) and investigating whether clinical dimensions of BD affect the accuracy of the classification.