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A growing number of structural neuroimaging studies have reported significant changes in gray matter density or volume and white matter microstructure in the adult human brain following training. Such reports appear consistent with animal studies of training-dependent structural plasticity showing changes in, for example, dendritic spines. However, given the microscopic nature of these changes in animals and the relatively low spatial resolution of MRI, it is unclear that such changes can be reliably detected in humans. Here, we critically evaluate the robustness of the current evidence in humans, focusing on the specificity, replicability, and the relationship of the reported changes with behavior. We find that limitations of experimental design, statistical methods, and methodological artifacts may underlie many of the reported effects, seriously undermining the evidence for training-dependent structural changes in adult humans. The most robust evidence, showing specificity of structural changes to training, task and brain region, shows changes in anterior hippocampal volume with exercise in elderly participants. We conclude that more compelling evidence and converging data from animal studies is required to substantiate structural changes in the adult human brain with training, especially in the neocortex.

Diurnal fluctuations in MRI measures of structural and functional properties of the brain have been reported recently. These fluctuations may have a physiological origin, since they have been detected using different MRI modalities, and cannot be explained by factors that are typically known to confound MRI measures. While preliminary evidence suggests that measures of structural properties of the brain based on diffusion tensor imaging (DTI) fluctuate as a function of time-of-day (TOD), the underlying mechanism has not been investigated. Here, we used a longitudinal within-subjects design to investigate the impact of time-of-day on DTI measures. In addition to using the conventional monoexponential tensor model to assess TOD-related fluctuations, we used a dual compartment tensor model that allowed us to directly assess if any change in DTI measures is due to an increase in CSF/free-water volume fraction or due to an increase in water diffusivity within the parenchyma. Our results show that Trace or mean diffusivity, as measured using the conventional monoexponential tensor model tends to increase systematically from morning to afternoon scans at the interface of grey matter/CSF, most prominently in the major fissures and the sulci of the brain. Interestingly, in a recent study of the glymphatic system, these same regions were found to show late enhancement after intrathecal injection of a CSF contrast agent. The increase in Trace also impacts DTI measures of diffusivity such as radial and axial diffusivity, but does not affect fractional anisotropy. The dual compartment analysis revealed that the increase in diffusivity measures from PM to AM was driven by an increase in the volume fraction of CSF-like free-water. Taken together, our findings provide important insight into the likely physiological origins of diurnal fluctuations in MRI measurements of structural properties of the brain. •Although diurnal fluctuations in MRI measures of structural and functional properties of the brain have been reported recently, the underlying physiological mechanisms are unclear.•Here, we first used diffusion tensor MRI to measures diurnal changes in diffusivity measures and found that Trace or mean diffusivity as measured using the conventional monoexponential tensor model, tends to increase systematically from morning to afternoon scans.•We then used a dual compartment tensor model that allowed us to directly assess if any change in DTI measures is due to an increase in CSF/free-water volume fraction and demonstrate that the increase in Trace from morning to afternoon can be explained by an increase in CSF-like free-water.•The time-of-day related changes are localized along the interface of grey matter and CSF and is most prominent along the major fissures and sulci of the brain, that have been associated with the glymphatic system.

Significance Diffusion-weighted MRI (DWI) tractography is widely used to map structural connections of the human brain in vivo and has been adopted by large-scale initiatives such as the human connectome project. Our results indicate that, even with high-quality data, DWI tractography alone is unlikely to provide an anatomically accurate map of the brain connectome. It is crucial to complement tractography results with a combination of histological or neurophysiological methods to map structural connectivity accurately. Our findings, however, do not diminish the importance of diffusion MRI as a noninvasive tool that offers important quantitative measures related to brain tissue microstructure and white matter architecture. Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

Measures of brain morphometry derived from T1-weighted (T1W) magnetic resonance imaging (MRI) are widely used to elucidate the relation between brain structure and function. However, the computation of T1W morphometric measures can be confounded by subject-related factors such as head motion and level of hydration. A recent study reported subtle yet significant changes in brain volume from morning to evening in a large group of patient populations as well as in healthy elderly individuals. In addition, there is a growing recognition that factors such as circadian rhythm can impact MRI measures of brain function and structure. Here, we provide a comprehensive assessment of the impact of time-of-day (TOD) on widely used measures of brain morphometry in a group of 19 healthy young adults. Our results show that (a) even in a small group of healthy adult volunteers, a highly significant reduction in apparent brain volume, from morning to evening, could be detected; (b) the apparent volume of all three major tissue compartments – gray matter, white matter, and cerebrospinal fluid – were influenced by TOD, and the magnitude of the TOD effect varied across the tissue compartments; (c) measures of cortical thickness, cortical surface area, and gray matter density computed with widely used neuroimaging software suites (i.e., FreeSurfer, FSL-VBM) were all affected by TOD, while other measures, such as curvature indices and sulcal depth, were not; and (d) the effect of TOD appeared to have a greater impact on morphometric measures of the frontal and temporal lobe than on other major lobes of the brain. Our results suggest that the TOD effect is a physiological phenomenon and that controlling for the effect of TOD is crucial for proper interpretation of apparent structural differences measured with T1W morphometry. •Time of day (TOD) impacts automatically derived measures of brain morphometry derived from T1W images.•The apparent volume of all major tissue compartments are influenced by TOD.•Measures of cortical thickness, cortical surface area and gray matter density are affected by TOD.•TOD has a greater impact on morphometric measures of the frontal and temporal lobe.

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain. •Organized international tractography challenge utilizing three validation datasets.•Anatomical accuracy of modern diffusion tractography techniques is limited.•Advancements are needed to overcome limited sensitivity/specificity of reconstructions.

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM. •DR-TAMAS is a novel diffusion tensor image registration and atlas creation method.•Deviatoric tensor and trace differences are considered for tensor-driven metrics.•Optional anatomical images can complement tensor-based registration.•All considered metrics are combined at a voxel level based on tissue type.•DR-TAMAS performs robustly in all brain regions including WM, GM, and CSF.

Diffusion tensor imaging (DTI) is the most widely used method for characterizing noninvasively structural and architectural features of brain tissues. However, the assumption of a Gaussian spin displacement distribution intrinsic to DTI weakens its ability to describe intricate tissue microanatomy. Consequently, the biological interpretation of microstructural parameters, such as fractional anisotropy or mean diffusivity, is often equivocal. We evaluate the clinical feasibility of assessing brain tissue microstructure with mean apparent propagator (MAP) MRI, a powerful analytical framework that efficiently measures the probability density function (PDF) of spin displacements and quantifies useful metrics of this PDF indicative of diffusion in complex microstructure (e.g., restrictions, multiple compartments). Rotation invariant and scalar parameters computed from the MAP show consistent variation across neuroanatomical brain regions and increased ability to differentiate tissues with distinct structural and architectural features compared with DTI-derived parameters. The return-to-origin probability (RTOP) appears to reflect cellularity and restrictions better than MD, while the non-Gaussianity (NG) measures diffusion heterogeneity by comprehensively quantifying the deviation between the spin displacement PDF and its Gaussian approximation. Both RTOP and NG can be decomposed in the local anatomical frame for reference determined by the orientation of the diffusion tensor and reveal additional information complementary to DTI. The propagator anisotropy (PA) shows high tissue contrast even in deep brain nuclei and cortical gray matter and is more uniform in white matter than the FA, which drops significantly in regions containing crossing fibers. Orientational profiles of the propagator computed analytically from the MAP MRI series coefficients allow separation of different fiber populations in regions of crossing white matter pathways, which in turn improves our ability to perform whole-brain fiber tractography. Reconstructions from subsampled data sets suggest that MAP MRI parameters can be computed from a relatively small number of DWIs acquired with high b-value and good signal-to-noise ratio in clinically achievable scan durations of less than 10min. The neuroanatomical consistency across healthy subjects and reproducibility in test–retest experiments of MAP MRI microstructural parameters further substantiate the robustness and clinical feasibility of this technique. The MAP MRI metrics could potentially provide more sensitive clinical biomarkers with increased pathophysiological specificity compared to microstructural measures derived using conventional diffusion MRI techniques. [Display omitted] •In vivo MAP metrics show consistent neuroanatomical contrast in healthy subjects.•These microstructural scalar measures provide novel information compared with DTI.•MAP parameters can be reconstructed from diffusion datasets acquired in 10min.•These metrics derived from clinical data sets show good test–retest repeatability.

Prosopagnosics have impaired face recognition, but make relatively normal responses to face stimuli in core brain regions for face recognition. The authors now report that it is the connectivity among these regions that is being disrupted in the disorder. Using diffusion tensor imaging and tractography, we found that a disruption in structural connectivity in ventral occipito-temporal cortex may be the neurobiological basis for the lifelong impairment in face recognition that is experienced by individuals who suffer from congenital prosopagnosia. Our findings suggest that white-matter fibers in ventral occipito-temporal cortex support the integrated function of a distributed cortical network that subserves normal face processing.

Resting-state functional connectivity (rsFC) between brain regions has been used for studying training-related changes in brain function during the offline period of skill learning. However, it is difficult to infer whether the observed training-related changes in rsFC measured between two scans occur as a consequence of task performance, whether they are specific to a given task, or whether they reflect confounding factors such as diurnal fluctuations in brain physiology that impact the MRI signal. Here, we sought to elucidate whether task-specific changes in rsFC are dissociable from time-of-day related changes by evaluating rsFC changes after participants were provided training in either a visuospatial task or a motor sequence task compared to a non-training condition. Given the nature of the tasks, we focused on changes in rsFC of the hippocampal and sensorimotor cortices after short-term training, while controlling for the effect of time-of-day. We also related the change in rsFC of task-relevant brain regions to performance improvement in each task. Our results demonstrate that, even in the absence of any experimental manipulation, significant changes in rsFC can be detected between two resting state functional MRI scans performed just a few hours apart, suggesting time-of-day has a significant impact on rsFC. However, by estimating the magnitude of the time-of-day effect, our findings also suggest that task-specific changes in rsFC can be dissociated from the changes attributed to time-of-day. Taken together, our results show that rsFC can provide insights about training-related changes in brain function during the offline period of skill learning. However, demonstrating the specificity of the changes in rsFC to a given task requires a rigorous experimental design that includes multiple active and passive control conditions, and robust behavioral measures.

In a critical review (Thomas and Baker, 2012), we argued for caution in evaluating reports of training-dependent adult structural plasticity measured with MRI. Here, we respond to the commentaries on our review, clarifying our position and addressing some of the specific criticisms raised.