White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI

The evolving role of diffusion magnetic resonance imaging in movement disorders

Significant advances have allowed diffusion magnetic resonance imaging (MRI) to evolve into a powerful tool in the field of movement disorders that can be used to study disease states and connectivity between brain regions. Diffusion MRI is a promising potential biomarker for Parkinson's disease and other forms of parkinsonism, and may allow the distinction of different forms of parkinsonism. Techniques such as tractography have contributed to our current thinking regarding the pathophysiology of dystonia and possible mechanisms of penetrance. Diffusion MRI measures could potentially assist in monitoring disease progression in Huntington's disease, and in uncovering the nature of the processes and structures involved the development of essential tremor. The ability to represent structural connectivity in vivo also makes diffusion MRI an ideal adjunctive tool for the surgical treatment of movement disorders. We review recent studies using diffusion MRI in movement disorders research and present the current state of the science as well as future directions.

Resilience and brain connectivity

A definition of resilience is the capacity to resist mental disorders despite exposure to stress. Little is known about its biological concomitants. In adults, biochemical and hormonal factors have been advocated. Smaller Corpus Callosum (CC) volume and lower Fractional Anisotropy (FA) have been observed in psychiatric and stress-related conditions. There is no Diffusion Tensor Imaging (DTI) study of resilience in adolescence, a critical lifetime period for neural and psychological maturation. We hypothesized that higher FA in the CC would characterize stress-resilient adolescents.

Methods

Three community groups were compared: resilient adolescents – with low risk of mental disorder despite high exposure to lifetime stress, adolescents at risk of mental disorder exposed to the same level of stress, and controls. Personality was assessed by NEO Five Factor Inventory (NEO-FFI) and cognitive function by a battery of tests. Voxelwise statistics of DTI values in CC were obtained using Tract-Based Spatial Statistic. Regional projections were identified by probabilistic tractography.

results

Higher FA values were detected in the anterior CC of resilient compared with both non-resilient and control adolescents. FA values varied according to resilience capacity. Regional changes in CC were in regions that project onto anterior cingulated and frontal cortex. Neuroticism and three other personality factors differentiated at risk adolescents from the other two groups.

Conclusion

High FA was detected in resilient adolescents in an anterior CC region projecting to frontal areas subserving cognitive resources. Psychiatric risk in adolescents was associated with personality characteristics. Resilience in adolescence may be a dimension embedding white matter features.

High-Resolution MR Imaging of the Human Brainstem In vivo at 7 Tesla

The human brainstem, which comprises a multitude of axonal nerve fibers and nuclei, plays an important functional role in the human brain. Depicting its anatomy non-invasively with high spatial resolution may thus in turn help to better relate normal and pathological anatomical variations to medical conditions as well as neurological and peripheral functions. We explored the potential of high-resolution magnetic resonance imaging (MRI) at 7 T for depicting the intricate anatomy of the human brainstem in vivo by acquiring and generating images with multiple contrasts: T 2-weighted images, quantitative maps of longitudinal relaxation rate (R 1 maps) and effective transverse relaxation rate ([Formula: see text] maps), magnetic susceptibility maps, and direction-encoded track-density images. Images and quantitative maps were compared with histological stains and anatomical atlases to identify nerve nuclei and nerve fibers. Among the investigated contrasts, susceptibility maps displayed the largest number of brainstem structures. Contrary to R 1 maps and T 2-weighted images, which showed rather homogeneous contrast, [Formula: see text] maps, magnetic susceptibility maps, and track-density images clearly displayed a multitude of smaller and larger fiber bundles. Several brainstem nuclei were identifiable in sections covering the pons and medulla oblongata, including the spinal trigeminal nucleus and the reticulotegmental nucleus on magnetic susceptibility maps as well as the inferior olive on R 1, [Formula: see text], and susceptibility maps. The substantia nigra and red nuclei were visible in all contrasts. In conclusion, high-resolution, multi-contrast MR imaging at 7 T is a versatile tool to non-invasively assess the individual anatomy and tissue composition of the human brainstem.

White matter alterations at 33-year follow-up in adults with childhood attention-deficit/hyperactivity disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is increasingly conceived as reflecting altered functional and structural brain connectivity. The latter can be addressed with diffusion tensor imaging (DTI). We examined fractional anisotropy (FA), a DTI index related to white matter structural properties, in adult male subjects diagnosed with ADHD in childhood (probands) and matched control subjects without childhood ADHD. Additionally, we contrasted FA among probands with and without current ADHD in adulthood and control subjects.

METHODS

Participants were from an original cohort of 207 boys and 178 male control subjects. At 33-year follow-up, analyzable DTI scans were obtained in 51 probands (41.3 ± 2.8 yrs) and 66 control subjects (41.2 ± 3.1 yrs). Voxel-based FA was computed with tract-based spatial statistics, controlling for multiple comparisons.

RESULTS

Probands with childhood ADHD exhibited significantly lower FA than control subjects without childhood ADHD in the right superior and posterior corona radiata, right superior longitudinal fasciculus, and in a left cluster including the posterior thalamic radiation, the retrolenticular part of the internal capsule, and the sagittal stratum (p<.05, corrected). Fractional anisotropy was significantly decreased relative to control subjects in several tracts in both probands with current and remitted ADHD, who did not differ significantly from each other. Fractional anisotropy was not significantly increased in probands in any region.

CONCLUSIONS

Decreased FA in adults with childhood ADHD regardless of current ADHD might be an enduring trait of ADHD. White matter tracts with decreased FA connect regions involved in high-level as well as sensorimotor functions, suggesting that both types of processes are involved in the pathophysiology of ADHD.

Sparse solution of fiber orientation distribution function by diffusion decomposition

Fiber orientation is the key information in diffusion tractography. Several deconvolution methods have been proposed to obtain fiber orientations by estimating a fiber orientation distribution function (ODF). However, the L₂ regularization used in deconvolution often leads to false fibers that compromise the specificity of the results. To address this problem, we propose a method called diffusion decomposition, which obtains a sparse solution of fiber ODF by decomposing the diffusion ODF obtained from q-ball imaging (QBI), diffusion spectrum imaging (DSI), or generalized q-sampling imaging (GQI). A simulation study, a phantom study, and an in-vivo study were conducted to examine the performance of diffusion decomposition. The simulation study showed that diffusion decomposition was more accurate than both constrained spherical deconvolution and ball-and-sticks model. The phantom study showed that the angular error of diffusion decomposition was significantly lower than those of constrained spherical deconvolution at 30° crossing and ball-and-sticks model at 60° crossing. The in-vivo study showed that diffusion decomposition can be applied to QBI, DSI, or GQI, and the resolved fiber orientations were consistent regardless of the diffusion sampling schemes and diffusion reconstruction methods. The performance of diffusion decomposition was further demonstrated by resolving crossing fibers on a 30-direction QBI dataset and a 40-direction DSI dataset. In conclusion, diffusion decomposition can improve angular resolution and resolve crossing fibers in datasets with low SNR and substantially reduced number of diffusion encoding directions. These advantages may be valuable for human connectome studies and clinical research.

Predicting planning performance from structural connectivity between left and right mid-dorsolateral prefrontal cortex: moderating effects of age during postadolescence and midadulthood

Complex cognitive abilities such as planning are known to critically rely on activity of bilateral mid-dorsolateral prefrontal cortex (mid-dlPFC). However, the functional relevance of the structural connectivity between left and right mid-dlPFC is yet unknown. Here, we applied global tractography to derive streamline counts as estimates of the structural connectivity between mid-dlPFC homologs and related it to planning performance in the Tower of London task across early to midadulthood, assuming a moderating effect of age. Multiple regression analyses with interaction effects revealed that streamline counts between left and right mid-dlPFC were negatively associated with planning performance specifically in early postadolescence. From the fourth life decade on, there was a trend for a reversed, positive association. These differential findings were corroborated by converging results from fractional anisotropy and white-matter density estimates in the genu of the corpus callosum where fibers connecting mid-dlPFC homologs traversed. Moreover, the results for streamline counts were regionally specific, marking the strength of mid-dlPFC connectivity as critical in predicting interindividual differences in planning performance across different stages of adulthood. Taken together, present findings provide first evidence for nonadditive effects of age on the relation between complex cognitive abilities and the structural connectivity of mid-dlPFC homologs.

Cortical grey matter and subcortical white matter brain microstructural changes in schizophrenia are localised and age independent: a case-control diffusion tensor imaging study

It is still unknown whether the structural brain impairments that characterize schizophrenia (SZ) worsen during the lifetime. Here, we aimed to describe age-related microstructural brain changes in cortical grey matter and subcortical white matter of patients affected by SZ. In this diffusion tensor imaging study, we included 69 patients diagnosed with SZ and 69 healthy control (HC) subjects, age and gender matched. We carried out analyses of covariance, with diagnosis as fixed factor and brain diffusion-related parameters as dependent variables, and controlled for the effect of education. White matter fractional anisotropy decreased in the entire age range spanned (18–65 years) in both SZ and HC and was significantly lower in younger patients with SZ, with no interaction (age by diagnosis) effect in fiber tracts including corpus callosum, corona radiata, thalamic radiations and external capsule. Also, grey matter mean diffusivity increased in the entire age range in both SZ and HC and was significantly higher in younger patients, with no age by diagnosis interaction in the left frontal operculum cortex, left insula and left planum polare and in the right temporal pole and right intracalcarine cortex. In individuals with SZ we found that localized brain cortical and white matter subcortical microstructural impairments appear early in life but do not worsen in the 18–65 year age range.

Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: a diffusion tensor imaging study

To investigate whether there are global white matter (WM) differences between autistic and healthy adults, we performed diffusion tensor imaging (DTI) in 14 male adults with Asperger syndrome (AS) and 19 gender-, age-, and intelligence quotient-matched controls. We focused on individuals with high-functioning autism spectrum disorder (ASD), AS, to decrease heterogeneity caused by large variation in the cognitive profile. Previous DTI studies of ASD have mainly focused on finding local changes in fractional anisotropy (FA) and mean diffusivity (MD), two indexes used to characterize microstructural properties of WM. Although the local or voxel-based approaches may be able to provide detailed information in terms of location of the observed differences, such results are known to be highly sensitive to partial volume effects, registration errors, or placement of the regions of interest. Therefore, we performed global histogram analyses of (a) whole-brain tractography results and (b) skeletonized WM masks. In addition to the FA and MD, the planar diffusion coefficient (CP) was computed as it can provide more specific information of the complexity of the neural structure. Our main finding indicated that adults with AS had higher mean FA values than controls. A less complex neural structure in adults with AS could have explained the results, but no significant difference in CP was found. Our results suggest that there are global abnormalities in the WM tissue of adults with AS.

Modeling healthy male white matter and myelin development: 3 through 60months of age

An emerging hypothesis in developmental and behavioral disorders is that they arise from disorganized brain messaging or reduced connectivity. Given the importance of myelin to efficient brain communication, characterization of myelin development in infancy and childhood may provide salient information related to early connectivity deficits. In this work, we investigate regional and whole brain growth trajectories of the myelin water fraction, a quantitative magnetic resonance imaging measure sensitive and specific to myelin content, in data acquired from 122 healthy male children from 3 to 60 months of age. We examine common growth functions to find the most representative model of myelin maturation and subsequently use the best of these models to develop a continuous population-averaged, four-dimensional model of normative myelination. Through comparisons with an independent sample of 63 male children across the same age span, we show that the developed model is representative of this population. This work contributes to understanding the trajectory of myelination in healthy infants and toddlers, furthering our knowledge of early brain development, and provides a model that may be useful for identifying developmental abnormalities.

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Proposes various growth models for modeling MWF trajectories

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Demonstrates modeling of MWF trajectories of 122 male infants under 5 years

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Statistically compares tested models using Bayesian information criterion

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Generates first four-dimensional model of whole-brain myelination of early childhood

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Discusses the utility of developed model

Imaging structural and functional brain networks in temporal lobe epilepsy

Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies

Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques.

Brain morphometry shows effects of long-term musical practice in middle-aged keyboard players

To what extent does musical practice change the structure of the brain? In order to understand how long-lasting musical training changes brain structure, 20 male right-handed, middle-aged professional musicians and 19 matched controls were investigated. Among the musicians, 13 were pianists or organists with intensive practice regimes. The others were either music teachers at schools or string instrumentalists, who had studied the piano at least as a subsidiary subject, and practiced less intensively. The study was based on T1-weighted MR images, which were analyzed using deformation-based morphometry. Cytoarchitectonic probabilistic maps of cortical areas and subcortical nuclei as well as myeloarchitectonic maps of fiber tracts were used as regions of interest to compare volume differences in the brains of musicians and controls. In addition, maps of voxel-wise volume differences were computed and analyzed. Musicians showed a significantly better symmetric motor performance as well as a greater capability of controlling hand independence than controls. Structural MRI-data revealed significant volumetric differences between the brains of keyboard players, who practiced intensively and controls in right sensorimotor areas and the corticospinal tract as well as in the entorhinal cortex and the left superior parietal lobule. Moreover, they showed also larger volumes in a comparable set of regions than the less intensively practicing musicians. The structural changes in the sensory and motor systems correspond well to the behavioral results, and can be interpreted in terms of plasticity as a result of intensive motor training. Areas of the superior parietal lobule and the entorhinal cortex might be enlarged in musicians due to their special skills in sight-playing and memorizing of scores. In conclusion, intensive and specific musical training seems to have an impact on brain structure, not only during the sensitive period of childhood but throughout life.

Connectivity patterns of pallidal DBS electrodes in focal dystonia: a diffusion tensor tractography study

Deep brain stimulation (DBS) of the internal pallidal segment (GPi: globus pallidus internus) is gold standard treatment for medically intractable dystonia, but detailed knowledge of mechanisms of action is still not available. There is evidence that stimulation of ventral and dorsal GPi produces opposite motor effects. The aim of this study was to analyse connectivity profiles of ventral and dorsal GPi. Probabilistic tractography was initiated from DBS electrode contacts in 8 patients with focal dystonia and connectivity patterns compared. We found a considerable difference in anterior-posterior distribution of fibres along the mesial cortical sensorimotor areas between the ventral and dorsal GPi connectivity. This finding of distinct GPi connectivity profiles further confirms the clinical evidence that the ventral and dorsal GPi belong to different functional and anatomic motor subsystems. Their involvement could play an important role in promoting clinical DBS effects in dystonia.

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Diffusion-weighted imaging (DWI) allows imaging the geometry of water diffusion in biological tissues. However, DW images are noisy at high b-values and acquisitions are slow when using a large number of measurements, such as in Diffusion Spectrum Imaging (DSI). This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality. To capture the structure of DWI data, we use sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity. The method learns a dictionary of diffusion profiles on all the DW images at the same time and then scales to full brain data. Its performance is investigated with simulations and two real DSI datasets. We obtain better signal estimates from noisy measurements than by applying mirror symmetry through the q-space origin, Gaussian denoising or state-of-the-art non-local means denoising. Using a high-resolution dictionary learnt on another subject, we show that we can reduce the number of images acquired while still generating high resolution DSI data. Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible with approximately 40 measurements. This opens important perspectives for the connectomics community using DSI.

Tractography of the corpus callosum in Huntington's disease

White matter abnormalities have been shown in presymptomatic and symptomatic Huntington's disease (HD) subjects using Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) methods. The largest white matter tract, the corpus callosum (CC), has been shown to be particularly vulnerable; however, little work has been done to investigate the regional specificity of tract abnormalities in the CC. Thus, this study examined the major callosal tracts by applying DTI-based tractography. Using TrackVis, a previously defined region of interest tractography method parcellating CC into seven major tracts based on target region was applied to 30 direction DTI data collected from 100 subjects: presymptomatic HD (Pre-HD) subjects (n=25), HD patients (n=25) and healthy control subjects (n=50). Tractography results showed decreased fractional anisotropy (FA) and increased radial diffusivity (RD) across broad regions of the CC in Pre-HD subjects. Similar though more severe deficits were seen in HD patients. In Pre-HD and HD, callosal FA and RD were correlated with Disease Burden/CAG repeat length as well as motor (UHDRSI) and cognitive (URDRS2) assessments. These results add evidence that CC pathways are compromised prior to disease onset with possible demyelination occurring early in the disease and suggest that CAG repeat length is a contributing factor to connectivity deficits. Furthermore, disruption of these callosal pathways potentially contributes to the disturbances of motor and cognitive processing that characterize HD.

Diffusion imaging-based subdivision of the human hypothalamus: a magnetic resonance study with clinical implications

The hypothalamus and its subdivisions are involved in many neuropsychiatric conditions such as affective disorders, schizophrenia, or narcolepsy, but parcellations of hypothalamic subnuclei have hitherto been feasible only with histological techniques in postmortem brains. In an attempt to map subdivisions of the hypothalamus in vivo, we analyzed the directionality information from high-resolution diffusion-weighted magnetic resonance images of healthy volunteers. We acquired T1-weighted and diffusion-weighted scans in ten healthy subjects at 3 T. In the T1-weighted images, we manually delineated an individual mask of the hypothalamus in each subject and computed in the co-registered diffusion-weighted images the similarity of the principal diffusion direction for each pair of mask voxels. By clustering the similarity matrix into three regions with a k-means algorithm, we obtained an anatomically coherent arrangement of subdivisions across hemispheres and subjects. In each hypothalamus mask, we found an anterior region with dorsoventral principal diffusion direction, a posteromedial region with rostro-caudal direction, and a lateral region with mediolateral direction. A comparative analysis with microstructural hypothalamus parcellations from the literature reveals that each of these regions corresponds to a specific group of hypothalamic subnuclei as defined in postmortem brains. This is to our best knowledge the first in vivo study that attempts a delineation of hypothalamic subdivisions by clustering diffusion-weighted magnetic resonance imaging data. When applied in a larger sample of neuropsychiatric patients, a structural analysis of hypothalamic subnuclei should contribute to a better understanding of the pathogenesis of neuropsychiatric conditions such as affective disorders.

"Early to bed, early to rise": diffusion tensor imaging identifies chronotype-specificity

Sleep and wakefulness are crucial prerequisites for cognitive efficiency, the disturbances of which severely impact performance and mood as present e.g. after time zone traveling, in shift workers or patients with sleep or affective disorders. Based on their individual disposition to sleep and wakefulness, humans can be categorized as early (EC), late (LC) or intermediate (IC) chronotypes. While ECs tend to wake up early in the morning and find it difficult to remain awake beyond their usual bedtime, LCs go to bed late and have difficulties getting up. Beyond sleep/wake timings, chronotypes show distinct patterns of cognitive performance, gene expression, endocrinology and lifestyle. However, little is known about brain structural characteristics potentially underlying differences. Specifically, white matter (WM) integrity is crucial for intact brain function and has been related to various lifestyle habits, suggesting differences between chronotypes. Hence, the present study draws on Diffusion Tensor Imaging as a powerful tool to non-invasively probe WM architecture in 16 ECs, 23 LCs and 20 ICs. Track-based spatial statistics highlight that LCs were characterized by WM differences in the frontal and temporal lobes, cingulate gyrus and corpus callosum. Results are discussed in terms of findings reporting late chronotypes to exhibit a chronic form of jet lag accompanied with sleep disturbances, vulnerability to depression and higher consumption of nicotine and alcohol. This study has far-reaching implications for health and the economy. Ideally, work schedules should fit in with chronotype-specificity whenever possible.

Polygenic risk and white matter integrity in individuals at high risk of mood disorder

BACKGROUND

Bipolar disorder (BD) and major depressive disorder (MDD) are highly heritable and genetically overlapping conditions characterized by episodic elevation and/or depression of mood. Both demonstrate abnormalities in white matter integrity, measured with diffusion tensor magnetic resonance imaging, that are also heritable. However, it is unclear how these abnormalities relate to the underlying genetic architecture of each disorder. Genome-wide association studies have demonstrated a significant polygenic contribution to BD and MDD, where risk is attributed to the summation of many alleles of small effect. Determining the effects of an overall polygenic risk profile score on neuroimaging abnormalities might help to identify proxy measures of genetic susceptibility and thereby inform models of risk prediction.

METHODS

In the current study, we determined the extent to which common genetic variation underlying risk to mood disorders (BD and MDD) was related to fractional anisotropy, an index of white matter integrity. This was conducted in unaffected individuals at familial risk of mood disorder (n = 70) and comparison subjects (n = 62). Polygenic risk scores were calculated separately for BD and MDD on the basis of genome-wide association study data from the Psychiatric GWAS Consortia.

RESULTS

We report that a higher polygenic risk allele load for MDD was significantly associated with decreased white matter integrity across both groups in a large cluster, with a peak in the right-sided superior longitudinal fasciculus.

CONCLUSIONS

These findings suggest that the polygenic approach to examining brain imaging data might be a useful means of identifying traits linked to the genetic risk of mood disorders.

Increased global and local efficiency of human brain anatomical networks detected with FLAIR-DTI compared to non-FLAIR-DTI

Diffusion-weighted MRI (DW-MRI), the only non-invasive technique for probing human brain white matter structures in vivo, has been widely used in both fundamental studies and clinical applications. Many studies have utilized diffusion tensor imaging (DTI) and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF), which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR) technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI) techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.

Anatomical brain networks on the prediction of abnormal brain states

Graph-based brain anatomical network analysis models the brain as a graph whose nodes represent structural/functional regions, whereas the links between them represent nervous fiber connections. Initial studies of brain anatomical networks using this approach were devoted to describe the key organizational principles of the normal brain, while current trends seem to be more focused on detecting network alterations associated to specific brain disorders. Anatomical networks reconstructed using diffusion-weighed magnetic resonance-imaging techniques can be particularly useful in predicting abnormal brain states in which the white matter structure and, subsequently, the interconnections between gray matter regions are altered (e.g., due to the presence of diseases such as schizophrenia, stroke, multiple sclerosis, and dementia). This article offers an overview from early gross connectional anatomy explorations until more recent advances on anatomical brain network reconstruction approaches, with a specific focus on how the latter move toward the prediction of abnormal brain states. While anatomical graph-based predictor approaches are still at an early stage, they bear promising implications for individualized clinical diagnosis of neurological and psychiatric disorders, as well as for neurodevelopmental evaluations and subsequent assisted creation of educational strategies related to specific cognitive disorders.

Assessment of structural connectivity in the preterm brain at term equivalent age using diffusion MRI and T2 relaxometry: a network-based analysis

Preterm birth is associated with a high prevalence of adverse neurodevelopmental outcome. Non-invasive techniques which can probe the neural correlates underpinning these deficits are required. This can be achieved by measuring the structural network of connections within the preterm infant's brain using diffusion MRI and tractography. We used diffusion MRI and T₂ relaxometry to identify connections with altered white matter properties in preterm infants compared to term infants. Diffusion and T₂ data were obtained from 9 term neonates and 18 preterm-born infants (born <32 weeks gestational age) at term equivalent age. Probabilistic tractography incorporating multiple fibre orientations was used in combination with the Johns Hopkins neonatal brain atlas to calculate the structural network of connections. Connections of altered diffusivity or T₂, as well as their relationship with gestational age at birth and postmenstrual age at the time of MRI, were identified using the network based statistic framework. A total of 433 connections were assessed. FA was significantly reduced in 17, and T₂ significantly increased in 18 connections in preterm infants, following correction for multiple comparisons. Cortical networks associated with affected connections mainly involved left frontal and temporal cortical areas: regions which are associated with working memory, verbal comprehension and higher cognitive function – deficits which are often observed later in children and adults born preterm. Gestational age at birth correlated with T₂, but not diffusion in several connections. We found no association between diffusion or T₂ and postmenstrual age at the time of MRI in preterm infants. This study demonstrates that alterations in the structural network of connections can be identified in preterm infants at term equivalent age, and that incorporation of non-diffusion measures such as T₂ in the connectome framework provides complementary information for the assessment of brain development.

Attention-deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development

Differential core symptoms and treatment responses are associated with the pure versus comorbid forms of attention-deficit/hyperactivity disorder (ADHD). However, comorbidity has largely been unaccounted for in neuroimaging studies of ADHD. We used diffusional kurtosis imaging to investigate gray matter (GM) and white matter (WM) microstructure of children and adolescents with ADHD (n = 22) compared to typically developing controls (TDC, n = 27) and examined whether differing developmental patterns are related to comorbidity. The ADHD group (ADHD-mixed) consisted of subgroups with and without comorbidity (ADHD-comorbid, n = 11; ADHD-pure, n = 11, respectively). Age-related changes and group differences in cerebral microstructure of the ADHD-mixed group and each ADHD subgroup were compared to TDC. Whole-brain voxel-based analyses with mean kurtosis (MK) and mean diffusivity (MD) metrics were conducted to probe GM and WM. Tract-based spatial statistics analyses of WM were performed with MK, MD, fractional anisotropy, and directional (axial, radial) kurtosis and diffusivity metrics. ADHD-pure patients lacked significant age-related changes in GM and WM microstructure that were observed globally in TDC and had significantly greater WM microstructural complexity than TDC in bilateral frontal and parietal lobes, insula, corpus callosum, and right external and internal capsules. Including ADHD patients with diverse comorbidities in analyses masked these findings. A distinct atypical age-related trajectory and aberrant regional differences in brain microstructure were detected in ADHD without comorbidity. Our results suggest that different phenotypic manifestations of ADHD, defined by the presence or absence of comorbidity, differ in cerebral microstructural markers.

White matter fiber tracking directed by interpolating splines and a methodological framework for evaluation

Image-based tractography of white matter (WM) fiber bundles in the brain using diffusion weighted MRI (DW-MRI) has become a useful tool in basic and clinical neuroscience. However, proper tracking is challenging due to the anatomical complexity of fiber pathways, the coarse resolution of clinically applicable whole-brain in vivo imaging techniques, and the difficulties associated with verification. In this study we introduce a new tractography algorithm using splines (denoted Spline). Spline reconstructs smooth fiber trajectories iteratively, in contrast to most other tractography algorithms that create piecewise linear fiber tract segments, followed by spline fitting. Using DW-MRI recordings from eight healthy elderly people participating in a longitudinal study of cognitive aging, we compare our Spline algorithm to two state-of-the-art tracking methods from the TrackVis software suite. The comparison is done quantitatively using diffusion metrics (fractional anisotropy, FA), with both (1) tract averaging, (2) longitudinal linear mixed-effects model fitting, and (3) detailed along-tract analysis. Further validation is done on recordings from a diffusion hardware phantom, mimicking a coronal brain slice, with a known ground truth. Results from the longitudinal aging study showed high sensitivity of Spline tracking to individual aging patterns of mean FA when combined with linear mixed-effects modeling, moderately strong differences in the along-tract analysis of specific tracts, whereas the tract-averaged comparison using simple linear OLS regression revealed less differences between Spline and the two other tractography algorithms. In the brain phantom experiments with a ground truth, we demonstrated improved tracking ability of Spline compared to the two reference tractography algorithms being tested.

Structural changes in left fusiform areas and associated fiber connections in children with abacus training: evidence from morphometry and tractography

Evidence supports the notion that the fusiform gyrus (FG), as an integral part of the ventral occipitotemporal junction, is involved widely in cognitive processes as perceiving faces, objects, places or words, and this region also might represent the visual form of an abacus in the abacus-based mental calculation process. The current study uses a combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) analysis to test whether long-term abacus training could induce structural changes in the left FG and in the white matter (WM) tracts distribution connecting with this region in school children. We found that, abacus-trained children exhibited significant smaller gray matter (GM) volume than controls in the left FG. And the connectivity mapping identified left forceps major as a key pathway connecting left FG with other brain areas in the trained group, but not in the controls. Furthermore, mean fractional anisotropy (FA) values within left forceps major were significantly increased in the trained group. Interestingly, a significant negative correlation was found in the trained group between the GM volume in left FG and the mean FA value in left forceps major, suggesting an inverse effect of the reported GM and WM structural changes. In the control group, a positive correlation between left FG GM volume and tract FA was found as well. This analysis visualized the group level differences in GM volume, FA and fiber tract between the abacus-trained children and the controls, and provided the first evidence that GM volume change in the left FG is intimately linked with the micro-structural properties of the left forceps major tracts. The present results demonstrate the structural changes in the left FG from the intracortical GM to the subcortical WM regions and provide insights into the neural mechanism of structural plasticity induced by abacus training.

Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke

Building a human connectome database has recently attracted the attention of many researchers, although its application to individual subjects has yet to be explored. In this study, we acquired diffusion spectrum imaging of 90 subjects and showed that this dataset can be used as a norm to examine pathways with deviant connectivity in individuals. This analytical approach, termed diffusion MRI connectometry, was realized by reconstructing patient data to a common stereotaxic space and calculating the percentile rank of the diffusion quantities with respect to those of the norm. The affected tracks were constructed with deterministic tractography using the local tract orientations with substantially low percentile ranks as seeds. To demonstrate the performance of the connectometry, we applied it to 7 patients with chronic stroke and compared the results with lesions shown on T₂-weighted images, apparent diffusion coefficient (ADC) maps, and fractional anisotropy (FA) maps, as well as clinical manifestations. The results showed that the affected tracks revealed by the connectometry corresponded well with the stroke lesions shown on T₂-weighted images. Moreover, while the T₂-weighted images, as well as the ADC and FA maps, showed only the stroke lesions, connectometry revealed entire affected tracks, a feature that is potentially useful for diagnostic or prognostic evaluation. This unique capability may provide personalized information regarding the structural connectivity underlying brain development, plasticity, or disease in each individual subject.

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Diffusion MRI connectometry can identify tracks with decreased connectivity.

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T₂-weighted images, and ADC, and FA maps show only the stroke lesions.

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Diffusion MRI connectometry reveals the entire affected pathways.

General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults

General fluid-type intelligence (gF) reflects abstract reasoning and problem solving abilities, and is an important predictor for lifetime trajectories of cognition, and physical and mental health. Structural and functional neuroimaging studies have demonstrated the role of parieto-frontal gray matter, but the white matter (WM) underpinnings of gF and the contribution of individual gF components to gF-WM relationship still need to be explored. The aim of this study was to characterize, in a sample of 100 healthy middle-aged and old subjects (mean=63.8 years), the relationship between gF and indices of WM structure obtained from diffusion tensor magnetic resonance imaging (DT-MRI) (fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)). gF was estimated by principal component analysis including measures of episodic memory, reasoning, and processing speed. Tract-based spatial statistics and permutation-based inference statistics were used to test the association between gF and WM indices, while controlling for the effect of age and sex. We hypothesized a positive relationship between gF and WM structure. Based on previous studies, we further hypothesized that this relationship was heavily influenced by the processing speed component of gF. We found a robust relationship between gF and DT-MRI measures of FA, RD and MD in all major WM tracts. Higher gF score was related to higher degree of WM integrity, in middle-aged as well as old individuals. Thus, the distributed relationship between gF and indices of WM microstructure is consistent with the notion that gF reflects efficient signaling between cortical areas. Furthermore, analysis of relationships between WM measures and gF components revealed an association with information processing speed and reasoning ability, but not with episodic memory. Thus, although all subcomponents loaded high on gF factor, the speed-related components were most strongly associated with DT-MRI-derived measures. These results suggest that DT-MRI can be used to parse gF.

Connectivity-based parcellation of the human frontal pole with diffusion tensor imaging

The human frontal pole (FP) approximately corresponds to Brodmann's area 10 and is a highly differentiated cortical area with unique cytoarchitectonic characteristics. However, its functional diversity is highly suggestive of the existence of functional subregions. Based on anatomical connection patterns derived from diffusion tensor imaging data, we applied a spectral clustering algorithm to parcellate the human right FP into orbital (FPo), lateral (FPl), and medial (FPm) subregions. This parcellation scheme was validated by corresponding analyses of the left FP and right FP in another independent dataset. Both visual observation and quantitative comparison of the anatomical connection patterns of the three FP subregions revealed that the FPo showed greater connection probabilities to brain regions of the social emotion network (SEN), including the orbitofrontal cortex, temporal pole, and amygdala, the FPl showed stronger connections to the dorsolateral prefrontal cortex of the cognitive processing network (CPN), and the FPm showed stronger connections to brain areas of the default mode network (DMN), including the anterior cingulate cortex and medial prefrontal cortex. We further analyzed the resting-state functional connectivity patterns of the three FP subregions. Consistent with the findings of anatomical connection analyses, the FPo was functionally correlated with the SEN, the FPl was correlated with the CPN, and the FPm was correlated with the DMN. These findings suggest that the human FP includes three separable subregions with different anatomical and functional connectivity patterns and that these subregions are involved in different brain functional networks and serve different functions.

Brain connectivity and visual attention

Emerging hypotheses suggest that efficient cognitive functioning requires the integration of separate, but interconnected cortical networks in the brain. Although task-related measures of brain activity suggest that a frontoparietal network is associated with the control of attention, little is known regarding how components within this distributed network act together or with other networks to achieve various attentional functions. This review considers both functional and structural studies of brain connectivity, as complemented by behavioral and task-related neuroimaging data. These studies show converging results: The frontal and parietal cortical regions are active together, over time, and identifiable frontoparietal networks are active in relation to specific task demands. However, the spontaneous, low-frequency fluctuations of brain activity that occur in the resting state, without specific task demands, also exhibit patterns of connectivity that closely resemble the task-related, frontoparietal attention networks. Both task-related and resting-state networks exhibit consistent relations to behavioral measures of attention. Further, anatomical structure, particularly white matter pathways as defined by diffusion tensor imaging, places constraints on intrinsic functional connectivity. Lastly, connectivity analyses applied to investigate cognitive differences across individuals in both healthy and diseased states suggest that disconnection of attentional networks is linked to deficits in cognitive functioning, and in extreme cases, to disorders of attention. Thus, comprehensive theories of visual attention and their clinical translation depend on the continued integration of behavioral, task-related neuroimaging, and brain connectivity measures.

Connectomic approaches before the connectome

Connectome is a term with a short history but a long past. Since the origins of neuroscience the concept of a 'map of neural connections' has been a constant inspiring idea for those who believed the brain as the organ of intellect. A myriad of proto-connectome maps have been produced throughout the centuries, each one reflecting the theory and method of investigation that prevailed at the time. Even contemporary definitions of the connectome rest upon the formulation of a neuronal theory that has been proposed over a hundred years ago. So, what is new? In this article we attempt to trace the development of certain anatomical and physiological concepts at the origins of modern definitions of the connectome. We argue that compared to previous attempts current connectomic approaches benefit from a wealth of imaging methods that in part could justify the enthusiasm for finally succeeding in achieving the goal. One of the unique advantages of contemporary approaches is the possibility of using quantitative methods to define measures of connectivity where structure, function and behaviour are integrated and correlated. We also argue that many contemporary maps are inaccurate surrogates of the true anatomy and a comprehensive connectome of the human brain remains a far distant point in the history to come.

Imaging human connectomes at the macroscale

At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.

Networks of anatomical covariance

Functional imaging or diffusion-weighted imaging techniques are widely used to understand brain connectivity at the systems level and its relation to normal neurodevelopment, cognition or brain disorders. It is also possible to extract information about brain connectivity from the covariance of morphological metrics derived from anatomical MRI. These covariance patterns may arise from genetic influences on normal development and aging, from mutual trophic reinforcement as well as from experience-related plasticity. This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.

Fronto-tectal white matter connectivity mediates facilitatory effects of non-invasive neurostimulation on visual detection

The causal ability of pre-target FEF activity to modulate visual detection for perithreshold stimuli has been recently demonstrated in humans by means of non-invasive neurostimulation. Yet in spite of the network-distributed effects of these type of techniques, the white matter (WM) tracts and distant visual nodes contributing to such behavioral impact remain unknown. We hereby used individual data from a group of healthy human subjects, who received time-locked pulses of active or sham Transcranial Magnetic Stimulation (TMS) to the right Frontal Eye Field (FEF) region, and experienced increases in visual detection sensitivity. We then studied the extent to which interindividual differences in visual modulation might be dependent on the WM patterns linking the targeted area to other regions relevant for visuo-attentional behaviors. We report a statistically significant correlation between the probability of connection in a right fronto-tectal pathway (FEF-Superior Colliculus) and the modulation of visual sensitivity during a detection task. Our findings support the potential contribution of this pathway and the superior colliculus in the mediation of visual performance from frontal regions in humans. Furthermore, we also show the ability of a TMS/DTI correlational approach to contribute to the disambiguation of the specific long-range pathways driving network-wide neurostimulatory effects on behavior, anticipating their future role in guiding a more efficient use of focal neurostimulation.

The Human Connectome Project and beyond: initial applications of 300 mT/m gradients

The engineering of a 3 T human MRI scanner equipped with 300 mT/m gradients - the strongest gradients ever built for an in vivo human MRI scanner - was a major component of the NIH Blueprint Human Connectome Project (HCP). This effort was motivated by the HCP's goal of mapping, as completely as possible, the macroscopic structural connections of the in vivo healthy, adult human brain using diffusion tractography. Yet, the 300 mT/m gradient system is well suited to many additional types of diffusion measurements. Here, we present three initial applications of the 300 mT/m gradients that fall outside the immediate scope of the HCP. These include: 1) diffusion tractography to study the anatomy of consciousness and the mechanisms of brain recovery following traumatic coma; 2) q-space measurements of axon diameter distributions in the in vivo human brain and 3) postmortem diffusion tractography as an adjunct to standard histopathological analysis. We show that the improved sensitivity and diffusion-resolution provided by the gradients are rapidly enabling human applications of techniques that were previously possible only for in vitro and animal models on small-bore scanners, thereby creating novel opportunities to map the microstructure of the human brain in health and disease.

Imaging structural co-variance between human brain regions

Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.

Competing physiological pathways link individual differences in weight and abdominal adiposity to white matter microstructure

Being overweight or obese is associated with reduced white matter integrity throughout the brain. It is not yet clear which physiological systems mediate the association between inter-individual variation in adiposity and white matter. We tested whether composite indicators of cardiovascular, lipid, glucose, and inflammatory factors would mediate the adiposity-related variation in white matter microstructure, measured with diffusion tensor imaging on a group of neurologically healthy adults (N=155). A composite factor representing adiposity (comprised of body mass index and waist circumference) was associated with smaller fractional anisotropy and greater radial diffusivity throughout the brain, a pattern previously linked to myelin structure changes in non-human animal models. A similar global negative association was found for factors representing inflammation and, to a lesser extent, glucose regulation. In contrast, factors for blood pressure and dyslipidemia had positive associations with white matter in isolated brain regions. Taken together, these competing influences on the diffusion signal were significant mediators linking adiposity to white matter and explained up to fifty-percent of the adiposity-white matter variance. These results provide the first evidence for contrasting physiological pathways, a globally distributed immunity-linked negative component and a more localized vascular-linked positive component, that associate adiposity to individual differences in the microstructure of white matter tracts in otherwise healthy adults.

Fiber clustering versus the parcellation-based connectome

We compare two strategies for modeling the connections of the brain's white matter: fiber clustering and the parcellation-based connectome. Both methods analyze diffusion magnetic resonance imaging fiber tractography to produce a quantitative description of the brain's connections. Fiber clustering is designed to reconstruct anatomically-defined white matter tracts, while the parcellation-based white matter segmentation enables the study of the brain as a network. From the perspective of white matter segmentation, we compare and contrast the goals and methods of the parcellation-based and clustering approaches, with special focus on reviewing the field of fiber clustering. We also propose a third category of new hybrid methods that combine the aspects of parcellation and clustering, for joint analysis of connection structure and anatomy or function. We conclude that these different approaches for segmentation and modeling of the white matter can advance the neuroscientific study of the brain's connectivity in complementary ways.

Comparing connectomes across subjects and populations at different scales

Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis.

Structural connectomics in brain diseases

Imaging the connectome in vivo has become feasible through the integration of several rapidly developing fields of science and engineering, namely magnetic resonance imaging and in particular diffusion MRI on one side, image processing and network theory on the other side. This framework brings in vivo brain imaging closer to the real topology of the brain, contributing to narrow the existing gap between our understanding of brain structural organization on one side and of human behavior and cognition on the other side. Given the seminal technical progresses achieved in the last few years, it may be ready to tackle even greater challenges, namely exploring disease mechanisms. In this review we analyze the current situation from the technical and biological perspectives. First, we critically review the technical solutions proposed in the literature to perform clinical studies. We analyze for each step (i.e. MRI acquisition, network building and network statistical analysis) the advantages and potential limitations. In the second part we review the current literature available on a selected subset of diseases, namely, dementia, schizophrenia, multiple sclerosis and others, and try to extract for each disease the common findings and main differences between reports.

Connectivity architecture and subdivision of the human inferior parietal cortex revealed by diffusion MRI

The human inferior parietal cortex convexity (IPCC) is an important association area, which integrates auditory, visual, and somatosensory information. However, the structural organization of the IPCC is a controversial issue. For example, cytoarchitectonic parcellations reported in the literature range from 2 to 7 areas. Moreover, anatomical descriptions of the human IPCC are often based on experiments in the macaque monkey. In this study, we used diffusion-weighted magnetic resonance imaging combined with probabilistic tractography to quantify the connectivity of the human IPCC, and used this information to parcellate this cortex area. This provides a new structural map of the human IPCC, comprising 3 subareas (inferior parietal cortex anterior, IPC middle, and IPC posterior) of comparable size, in a rostro-caudal arrangement in the left and right hemispheres. Each subarea is characterized by a connectivity fingerprint, and the parcellation is similar to the subdivision reported for the macaque IPCC with 3 areas in a rostro-caudal arrangement (PF, PFG, and PG). However, the present study also reliably demonstrates new structural features in the connectivity pattern of the human IPCC, which are not known to exist in the macaque. This study quantifies intersubject variability by providing a population representation of the subarea arrangement and demonstrates the substantial lateralization of the connectivity patterns of the IPCC.

Diffusion weighted imaging of prefrontal cortex in prodromal Huntington's disease

Huntington's disease (HD) is a devastating neurodegenerative disease with no effective disease-modifying treatments. There is considerable interest in finding reliable indicators of disease progression to judge the efficacy of novel treatments that slow or stop disease onset before debilitating signs appear. Diffusion-weighted imaging (DWI) may provide a reliable marker of disease progression by characterizing diffusivity changes in white matter (WM) in individuals with prodromal HD. The prefrontal cortex (PFC) may play a role in HD progression due to its prominent striatal connections and documented role in executive function. This study uses DWI to characterize diffusivity in specific regions of PFC WM defined by FreeSurfer in 53 prodromal HD participants and 34 controls. Prodromal HD individuals were separated into three CAG-Age Product (CAP) groups (16 low, 22 medium, 15 high) that indexed baseline progression. Statistically significant increases in mean diffusivity (MD) and radial diffusivity (RD) among CAP groups relative to controls were seen in inferior and lateral PFC regions. For MD and RD, differences among controls and HD participants tracked with baseline disease progression. The smallest difference was for the low group and the largest for the high group. Significant correlations between Trail Making Test B (TMTB) and mean fractional anisotropy (FA) and/or RD paralleled group differences in mean MD and/or RD in several right hemisphere regions. The gradient of effects that tracked with CAP group suggests DWI may provide markers of disease progression in future longitudinal studies as increasing diffusivity abnormalities in the lateral PFC of prodromal HD individuals.

Early onset of cortical thinning in children with rolandic epilepsy

INTRODUCTION

Rolandic epilepsy, a childhood epilepsy associated with language impairments, was investigated for language-related cortical abnormalities.

METHODS

Twenty-four children with rolandic epilepsy and 24 controls (age 8-14 years) were recruited and underwent the Clinical Evaluation of Language Fundamentals test. Structural MRI was performed at 3 T (voxel size 1 × 1 × 1 mm(3)) for fully automated quantitative assessment of cortical thickness. Regression analysis was used to test for differences between patients and controls and to assess the effect of age and language indices on cortical thickness.

RESULTS

For patients the core language score (mean ± SD: 92 ± 18) was lower than for controls (106 ± 11, p = 0.0026) and below the norm of 100 ± 15 (p = 0.047). Patients showed specific impairments in receptive language index (87 ± 19, p = 0.002) and language content index (87 ± 18, p = 0.0016). Cortical thickness was reduced in patients (p < 0.05, multiple-comparisons corrected) in left perisylvian regions. Furthermore, extensive cortical thinning with age was found in predominantly left-lateralized frontal, centro-parietal and temporal regions. No associations were found between cortical thickness and language indices in the regions of aberrant cortex.

CONCLUSION

The cortical abnormalities described represent subtle but significant pathomorphology in this critical phase of brain development (8-14 years) and suggest that rolandic epilepsy should not be considered merely a benign condition. Future studies employing longitudinal designs are prompted for further investigations into cerebral abnormalities in RE and associations with cognitive impairment and development.

A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.

Prediction of selective serotonin reuptake inhibitor response using diffusion-weighted MRI

Pre-treatment differences in serotonergic binding between those who remit to antidepressant treatment and those who do not have been found using Positron Emission Tomography (PET). To investigate these differences, an exploratory study was performed using a second imaging modality, diffusion-weighted MRI (DW-MRI). Eighteen antidepressant-free subjects with Major Depressive Disorder received a 25-direction DW-MRI scan prior to 8 weeks of selective serotonin reuptake inhibitor treatment. Probabilistic tractography was performed between the midbrain/raphe and two target regions implicated in depression pathophysiology (amygdala and hippocampus). Average fractional anisotropy (FA) within the derived tracts was compared between SSRI remitters and non-remitters, and correlation between pre-treatment FA values and SSRI treatment outcome was assessed. Results indicate that average FA in DW-MRI-derived tracts to the right amygdala was significantly lower in non-remitters (0.55 ± 0.04) than remitters (0.61 ± 0.04, p < 0.01). In addition, there was a significant correlation between average FA in tracts to the right amygdala and SSRI treatment response. These relationships were found at a trend level when using the left amygdala as a tractography target. No significant differences were observed when using the hippocampus as target. These regional differences, consistent with previous PET findings, suggest that the integrity and/or number of white matter fibers terminating in the right amygdala may be compromised in SSRI non-remitters. Further, this study points to the benefits of multimodal imaging and suggests that DW-MRI may provide a pre-treatment signature of SSRI depression remission at 8 weeks.

The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter

Biophysical models that describe the outcome of white matter diffusion MRI experiments have various degrees of complexity. While the simplest models assume equal-sized and parallel axons, more elaborate ones may include distributions of axon diameters and axonal orientation dispersions. These microstructural features can be inferred from diffusion-weighted signal attenuation curves by solving an inverse problem, validated in several Monte Carlo simulation studies. Model development has been paralleled by microscopy studies of the microstructure of excised and fixed nerves, confirming that axon diameter estimates from diffusion measurements agree with those from microscopy. However, results obtained in vivo are less conclusive. For example, the amount of slowly diffusing water is lower than expected, and the diffusion-encoded signal is apparently insensitive to diffusion time variations, contrary to what may be expected. Recent understandings of the resolution limit in diffusion MRI, the rate of water exchange, and the presence of microscopic axonal undulation and axonal orientation dispersions may, however, explain such apparent contradictions. Knowledge of the effects of biophysical mechanisms on water diffusion in tissue can be used to predict the outcome of diffusion tensor imaging (DTI) and of diffusion kurtosis imaging (DKI) studies. Alterations of DTI or DKI parameters found in studies of pathologies such as ischemic stroke can thus be compared with those predicted by modelling. Observations in agreement with the predictions strengthen the credibility of biophysical models; those in disagreement could provide clues of how to improve them. DKI is particularly suited for this purpose; it is performed using higher b-values than DTI, and thus carries more information about the tissue microstructure. The purpose of this review is to provide an update on the current understanding of how various properties of the tissue microstructure and the rate of water exchange between microenvironments are reflected in diffusion MRI measurements. We focus on the use of biophysical models for extracting tissue-specific parameters from data obtained with single PGSE sequences on clinical MRI scanners, but results obtained with animal MRI scanners are also considered. While modelling of white matter is the central theme, experiments on model systems that highlight important aspects of the biophysical models are also reviewed.

PANDA: a pipeline toolbox for analyzing brain diffusion images

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.