Diffusion Magnetic Resonance Imaging Research Articles

Orientation selective stimulation with tetrahedral electrodes of the rat infralimbic cortex to indirectly target the amygdala.

Deep brain stimulation (DBS) is a rapidly developing therapeutic intervention with constantly expanding neurological and psychiatric indications. A major challenge for the approach is the precise targeting and limitation of the effect on the desired neural pathways. We have introduced a new approach, orientation selective stimulation (OSS) that allows free rotation of the induced electric field on a plane when using a probe with three parallel electrodes forming an equilateral triangle at the tip. Here, we expand the technique by introducing a tetrahedral stimulation probe that enables adjustment of the primary electric field direction freely at any angle in a 3D space around the stimulating probe. OSS in 3D will enable better targeting of the electric field according to the local brain anatomy. We tested its utility in a rat model of DBS for treatment-resistant depression. The stimulation directed to the subgenual anterior cingulate cortex (sgACC) has yielded dramatic improvement in individual patients suffering from therapy resistant depression, but no consistent benefit in larger series. This failure has been ascribed to the challenging anatomy of sgACC with several crossing neural tracts and individual differences in the local anatomy. We stimulated infralimbic cortex (IL), the rat analog of sgACC, and recorded local electrical responses in amygdala (AMG) that is monosynaptically connected to IL and plays a central role in emotional states. We further traced AMG-IL connections using a viral vector and tractography using diffusion magnetic resonance imaging (MRI). Finally, we mimicked the clinical situation by delivering sustained 130 Hz stimulation at IL at the most effective field orientation and followed changes in resting-state functional connectivity with IL using functional MRI. To help interpretation of responses in functional connectivity, we stimulated only the left IL, which we did not expect to evoke measurable changes in the rat behavior. The AMG evoked responses depended systematically on the IL stimulation field orientation and yielded the maximum response in near vertical field orientation in accordance with tractography. Sustained 130 Hz stimulation at a field orientation yielding the strongest AMG evoked responses increased functional connectivity between IL and AMG on the stimulation side. These findings suggest that OSS in 3D provides a new approach to optimize the DBS for every individual patient with a single stimulation probe implantation.

Deficits in naming pictures of objects are associated with glioma infiltration of the inferior longitudinal fasciculus: A study with diffusion MRI tractography, volumetric MRI, and neuropsychology.

It has been suggested that the inferior longitudinal fasciculus (ILF) may play an important role in several aspects of language processing such as visual object recognition, visual memory, lexical retrieval, reading, and specifically, in naming visual stimuli. In particular, the ILF appears to convey visual information from the occipital lobe to the anterior temporal lobe (ATL). However, direct evidence proving the essential role of the ILF in language and semantics remains limited and controversial. The first aim of this study was to prove that patients with a brain glioma damaging the left ILF would be selectively impaired in picture naming of objects; the second aim was to prove that patients with glioma infiltrating the ATL would not be impaired due to functional reorganization of the lexical retrieval network elicited by the tumor. We evaluated 48 right-handed patients with neuropsychological testing and magnetic resonance imaging (MRI) before and after surgery for resection of a glioma infiltrating aspects of the left temporal, occipital, and/or parietal lobes; diffusion tensor imaging (DTI) was acquired preoperatively in all patients. Damage to the ILF, inferior frontal occipital fasciculus (IFOF), uncinate fasciculus (UF), arcuate fasciculus (AF), and associated cortical regions was assessed by means of preoperative tractography and pre-/pos-toperative MRI volumetry. The association of fascicles damage with patients' performance in picture naming and three additional cognitive tasks, namely, verbal fluency (two verbal non-visual tasks) and the Trail Making Test (a visual attentional task), was evaluated. Nine patients were impaired in the naming test before surgery. ILF damage was demonstrated with tractography in six (67%) of these patients. The odds of having an ILF damage was 6.35 (95% CI: 1.27-34.92) times higher among patients with naming deficit than among those without it. The ILF was the only fascicle to be significantly associated with naming deficit when all the fascicles were considered together, achieving an adjusted odds ratio of 15.73 (95% CI: 2.30-178.16, p = .010). Tumor infiltration of temporal and occipital cortices did not contribute to increase the odd of having a naming deficit. ILF damage was found to be selectively associated with picture naming deficit and not with lexical retrieval assessed by means of verbal fluency. Early after surgery, 29 patients were impaired in naming objects. The association of naming deficit with percentage of ILF resection (assessed by 3D-MRI) was confirmed (beta = -56.78 ± 20.34, p = .008) through a robust multiple linear regression model; no significant association was found with damage of IFOF, UF or AF. Crucially, postoperative neuropsychological evaluation showed that naming scores of patients with tumor infiltration of the anterior temporal cortex were not significantly associated with the percentage of ILF damage (rho = .180, p > .999), while such association was significant in patients without ATL infiltration (rho = -.556, p = .004). The ILF is selectively involved in picture naming of objects; however, the naming deficits are less severe in patients with glioma infiltration of the ATL probably due to release of an alternative route that may involve the posterior segment of the AF. The left ILF, connecting the extrastriatal visual cortex to the anterior region of the temporal lobe, is crucial for lexical retrieval on visual stimulus, such as in picture naming. However, when the ATL is also damaged, an alternative route is released and the performance improves.

White matter microstructure changes in adults with major depressive disorder: evidence from diffusion magnetic resonance imaging

Major depressive disorder (MDD) is a serious psychiatric disorder marked by low mood and anhedonia. Understanding the neural mechanism of MDD is essential for the treatment of depression. White matter fibres, connecting different computational units in the brain, have an important effect on brain function; however, the mechanism of white matter fibre abnormality in MDD is still unclear. Our study expected to find white matter abnormalities associated with the frontal lobe and hippocampus in individuals with MDD. Using diffusion tensor imaging data and tract-based spatial statistics, we investigated the microstructural differences in white matter fibre tracts between 30 adults with MDD compared with 31 healthy controls, and calculated the association between MDD-related microstructural changes and illness duration. It was found that patients with MDD showed reduced fractional anisotropy in the genu and body of the corpus callosum, right corona radiata and part of the thalamic radiations, suggesting lower fibrous myelination levels in these regions; the decreased fractional anisotropy in these regions was associated with longer illness duration. Our results suggest that MDD may be associated with microstructural damage of key fibre tracts, which could provide insights into the understanding and treatment of MDD.

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation.

The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and analyze the anatomical area of CNs by selecting the reference streamlines in combination with ROIs-based (regions-of-interests) or clustering-based. However, due to the slender structure of CNs and the complex anatomical environment, single-modality data based on dMRI cannot provide a complete and accurate description, resulting in low accuracy or even failure of current algorithms in performing individualized CNs segmentation. In this work, we propose a novel multimodal deep-learning-based multi-class network for automated cranial nerves tract segmentation without using tractography, ROI placement or clustering, called CNTSeg. Specifically, we introduced T1w images, fractional anisotropy (FA) images, and fiber orientation distribution function (fODF) peaks into the training data set, and design the back-end fusion module which uses the complementary information of the interphase feature fusion to improve the segmentation performance. CNTSeg has achieved the segmentation of 5 pairs of CNs (i.e. optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial-vestibulocochlear nerve CN VII/VIII). Extensive comparisons and ablation experiments show promising results and are anatomically convincing even for difficult tracts. The code will be openly available athttps://github.com/IPIS-XieLei/CNTSeg.

A microstructure estimation Transformer inspired by sparse representation for diffusion MRI

Diffusion magnetic resonance imaging (dMRI) is an important tool in characterizing tissue microstructure based on biophysical models, which are typically multi-compartmental models with mathematically complex and highly non-linear forms. Resolving microstructures from these models with conventional optimization techniques is prone to estimation errors and requires dense sampling in the q-space with a long scan time. Deep learning based approaches have been proposed to overcome these limitations. Motivated by the superior performance of the Transformer in feature extraction than the convolutional structure, in this work, we present a learning-based framework based on Transformer, namely, a Microstructure Estimation Transformer with Sparse Coding (METSC) for dMRI-based microstructural parameter estimation. To take advantage of the Transformer while addressing its limitation in large training data requirement, we explicitly introduce an inductive bias-model bias into the Transformer using a sparse coding technique to facilitate the training process. Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage. The embedding stage is a Transformer-based structure that encodes the signal in a high-level space to ensure the core voxel of a patch is represented effectively. In the sparse representation stage, a dictionary is constructed by solving a sparse reconstruction problem that unfolds the Iterative Hard Thresholding (IHT) process. The mapping stage is essentially a decoder that computes the microstructural parameters from the output of the second stage, based on the weighted sum of normalized dictionary coefficients where the weights are also learned. We tested our framework on two dMRI models with downsampled q-space data, including the intravoxel incoherent motion (IVIM) model and the neurite orientation dispersion and density imaging (NODDI) model. The proposed method achieved up to 11.25 folds of acceleration while retaining high fitting accuracy for NODDI fitting, reducing the mean squared error (MSE) up to 70% compared with the previous q-space learning approach. METSC outperformed the other state-of-the-art learning-based methods, including the model-free and model-based methods. The network also showed robustness against noise and generalizability across different datasets. The superior performance of METSC indicates its potential to improve dMRI acquisition and model fitting in clinical applications.

Rich-club connectivity and structural connectome organization in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia.

Brain dysconnectivity has been posited as a biological marker of schizophrenia. Emerging schizophrenia connectome research has focused on rich-club organization, a tendency for brain hubs to be highly-interconnected but disproportionately vulnerable to dysconnectivity. However, less is known about rich-club organization in individuals at clinical high-risk for psychosis (CHR-P) and how it compares with abnormalities early in schizophrenia (ESZ). Combining diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), we examined rich-club and global network organization in CHR-P (n=41) and ESZ (n=70) relative to healthy controls (HC; n=74) after accounting for normal aging. To characterize rich-club regions, we examined rich-club MRI morphometry (thickness, surface area). We also examined connectome metric associations with symptom severity, antipsychotic dosage, and in CHR-P specifically, transition to a full-blown psychotic disorder. ESZ had fewer connections among rich-club regions (ps<.024) relative to HC and CHR-P, with this reduction specific to the rich-club even after accounting for other connections in ESZ relative to HC (ps<.048). There was also cortical thinning of rich-club regions in ESZ (ps<.013). In contrast, there was no strong evidence of global network organization differences among the three groups. Although connectome abnormalities were not present in CHR-P overall, CHR-P converters to psychosis (n=9) had fewer connections among rich-club regions (ps<.037) and greater modularity (ps<.037) compared to CHR-P non-converters (n=19). Lastly, symptom severity and antipsychotic dosage were not significantly associated with connectome metrics (ps<.012). Findings suggest that rich-club and connectome organization abnormalities are present early in schizophrenia and in CHR-P individuals who subsequently transition to psychosis.

Functional and Structural Properties of Interhemispheric Interaction between Bilateral Precentral Hand Motor Regions in a Top Wheelchair Racing Paralympian.

Long-term motor training can cause functional and structural changes in the human brain. Assessing how the training of specific movements affects specific parts of the neural circuitry is essential to understand better the underlying mechanisms of motor training-induced plasticity in the human brain. We report a single-case neuroimaging study that investigated functional and structural properties in a professional athlete of wheelchair racing. As wheelchair racing requires bilateral synchronization of upper limb movements, we hypothesized that functional and structural properties of interhemispheric interactions in the central motor system might differ between the professional athlete and controls. Functional and diffusion magnetic resonance imaging (fMRI and dMRI) data were obtained from a top Paralympian (P1) in wheelchair racing. With 23 years of wheelchair racing training starting at age eight, she holds an exceptional competitive record. Furthermore, fMRI and dMRI data were collected from three other paraplegic participants (P2-P4) with long-term wheelchair sports training other than wheelchair racing and 37 able-bodied control volunteers. Based on the fMRI data analyses, P1 showed activation in the bilateral precentral hand sections and greater functional connectivity between these sections during a right-hand unimanual task. In contrast, other paraplegic participants and controls showed activation in the contralateral hemisphere and deactivation in the ipsilateral hemisphere. Moreover, dMRI data analysis revealed that P1 exhibited significantly lower mean diffusivity along the transcallosal pathway connecting the bilateral precentral motor regions than control participants, which was not observed in the other paraplegic participants. These results suggest that long-term training with bilaterally synchronized upper-limb movements may promote bilateral recruitment of the precentral hand sections. Such recruitment may affect the structural circuitry involved in the interhemispheric interaction between the bilateral precentral regions. This study provides valuable evidence of the extreme adaptability of the human brain.

Childhood Disadvantage Moderates Late Midlife Default Mode Network Cortical Microstructure and Visual Memory Association.

Childhood disadvantage is a prominent risk factor for cognitive and brain aging. Childhood disadvantage is associated with poorer episodic memory in late midlife and functional and structural brain abnormalities in the default mode network (DMN). Although age-related changes in DMN are associated with episodic memory declines in older adults, it remains unclear if childhood disadvantage has an enduring impact on this later-life brain-cognition relationship earlier in the aging process. Here, within the DMN, we examined whether its cortical microstructural integrity-an early marker of structural vulnerability that increases the risk for future cognitive decline and neurodegeneration-is associated with episodic memory in adults at ages 56-66, and whether childhood disadvantage moderates this association. Cortical mean diffusivity (MD) obtained from diffusion magnetic resonance imaging was used to measure microstructural integrity in 350 community-dwelling men. We examined both visual and verbal episodic memory in relation to DMN MD and divided participants into disadvantaged and nondisadvantaged groups based on parental education and occupation. Higher DMN MD was associated with poorer visual memory but not verbal memory (β = -0.11, p = .040 vs β = -0.04, p = .535). This association was moderated by childhood disadvantage and was significant only in the disadvantaged group (β = -0.26, p = .002 vs β = -0.00, p = .957). Lower DMN cortical microstructural integrity may reflect visual memory vulnerability in cognitively normal adults earlier in the aging process. Individuals who experienced childhood disadvantage manifested greater vulnerability to cortical microstructure-related visual memory dysfunction than their nondisadvantaged counterparts who exhibited resilience in the face of low cortical microstructural integrity.

Localizing Delirium through Automated Lesion Segmentation and Neural Network Mapping (N1.001)

<h3>Objective:</h3> To determine whether delirium in acute ischemic stroke is localizable to specific neural networks. <h3>Background:</h3> Delirium is estimated to occur in one-fourth of patients with acute stroke. Despite its associated morbidity and mortality, prediction and pathophysiological understanding of delirium remains challenging. A recent meta-analysis of patients with delirium and stroke found increased risks of delirium in supratentorial, cortical, and anterior circulation strokes, raising the possibility of an underlying neural network for delirium. <h3>Design/Methods:</h3> We performed a retrospective cohort study of patients admitted to a comprehensive stroke center with acute ischemic stroke from January 2016 to April 2019. Patients were assessed for delirium by trained clinical nursing staff using the Confusion Assessment Method (CAM) framework. Acute stroke lesions from MRI diffusion weighted images (DWI) lesions were automatically segmented using a machine learning algorithm. Lesions were registered to a 2mm Montreal Neurological Institute template (MNI-152). We then used lesion network mapping to identify potential unifying brain networks for delirium in patients with acute stroke. <h3>Results:</h3> 907 scans from unique patients were analyzed. 274 patients had delirium (30.2%), 47.5% were women (431), and the mean age was 69.6 years old. In comparison to a general stroke cohort, patients with delirium had lesions connected to the right and left inferior frontal gyri and left middle temporal gyrus and left temporo-parietal junction (one-sample t-test, family-wise error p&lt;0.05). Sensitivity analysis showed that delirium was not associated with parietal, occipital, brainstem, and cerebellar lesions. <h3>Conclusions:</h3> Delirium in stroke localizes to a network connected to frontotemporal regions. These results have implications for prediction of delirium in patients with acute stroke, with the potential for targeting multimodal prevention. Future work will refine their specificity, however, the results suggest that frontotemporal networks may be particularly important for cardinal symptoms of delirium, a disorder of impaired attention and awareness <b>Disclosure:</b> Dr. Rhee has stock in NTAP. Dr. Rhee has stock in TSLA. Dr. Rhee has stock in BABA. Dr. Rhee has stock in ZM. Dr. Rhee has stock in GM. Dr. Rhee has stock in PFF. Dr. Rhee has stock in AMZN. Dr. Rhee has stock in META. Dr. Rhee has stock in GE. Dr. Rhee has received publishing royalties from a publication relating to health care. Dr. Rhee has received personal compensation in the range of $0-$499 for serving as a Director of the Hippocratic Forum with Abigail Adams Institute. Dr. Ferguson has nothing to disclose. Dr. Bonkhoff has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for NeuroImage Clinical (Elsevier). Mr. Patel has nothing to disclose. Dr. Bretzner has nothing to disclose. Dr. Hong has nothing to disclose. Dr. Ryan has nothing to disclose. Dr. Westover has stock in Beacon Biosignals. The institution of Dr. Westover has received research support from NIH. Dr. Westover has received publishing royalties from a publication relating to health care. Dr. Westover has a non-compensated relationship as a cofounder with Beacon Biosignals that is relevant to AAN interests or activities. Dr. Fox has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Wiley. Dr. Rost has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Stroke - AHA/ASA Journal. The institution of Dr. Rost has received research support from NIH. Dr. Rost has received publishing royalties from a publication relating to health care. The institution of Dr. Kimchi has received research support from NIH. The institution of an immediate family member of Dr. Kimchi has received research support from NIH.

Quantifying changes in local basal ganglia structural connectivity in the 6-hydroxydopamine model of Parkinson's Disease using correlational tractography.

In recent years, tractography based on diffusion magnetic resonance imaging (dMRI) has become a popular tool for studying microstructural changes resulting from brain diseases like Parkinson's Disease (PD). Quantitative anisotropy (QA) is a parameter that is used in deterministic fiber tracking as a measure of connection between brain regions. It remains unclear, however, if microstructural changes caused by lesioning the median forebrain bundle (MFB) to create a Parkinsonian rat model can be resolved using tractography based on ex-vivo diffusion MRI. This study aims to fill this gap and enable future mechanistic research on structural changes of the whole brain network rodent models of PD. Specifically, it evaluated the ability of correlational tractography to detect structural changes in the MFB of 6-hydroxydopamine (6-OHDA) lesioned rats. The findings reveal that correlational tractography can detect structural changes in lesioned MFB and differentiate between the 6-OHDA and control groups. Imaging results are supported by behavioral and histological evidence demonstrating that 6-OHDA lesioned rats were indeed Parkinsonian. The results suggest that QA and correlational tractography is appropriate to examine local structural changes in rodent models of neurodegenerative disease. More broadly, we expect that similar techniques may provide insight on how disease alters structure throughout the brain, and as a tool to optimize therapeutic interventions.

Brain transcriptome-wide association study implicates novel risk genes underlying schizophrenia risk

To identify risk genes whose expression are regulated by the reported risk variants and to explore the potential regulatory mechanism in schizophrenia (SCZ). We systematically integrated three independent brain expression quantitative traits (eQTLs) (CommonMind, GTEx, and BrainSeq Phase 2, a total of 1039 individuals) and GWAS data (56 418 cases and 78 818 controls), with the use of transcriptome-wide association study (TWAS). Diffusion magnetic resonance imaging was utilized to quantify the integrity of white matter bundles and determine whether polygenic risk of novel genes linked to brain structure was present in patients with first-episode antipsychotic SCZ. TWAS showed that eight risk genes (CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, PCDHA8, THOC7, and TYW5) reached transcriptome-wide significance (TWS) level. These findings were confirmed by an independent integrative approach (i.e. Sherlock). We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Gene expression analysis showed that several TWS genes (including CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, THOC7 and TYW5) were dysregulated in the dorsolateral prefrontal cortex of SCZ cases compared with controls. TWS genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and microglia. Finally, SCZ cases had a substantially greater TWS genes-based polygenic risk (PRS) compared to controls, and we showed that fractional anisotropy of the cingulum-hippocampus mediates the influence of TWS genes PRS on SCZ. Our findings identified novel SCZ risk genes and highlighted the importance of the TWS genes in frontal-limbic dysfunctions in SCZ, indicating possible therapeutic targets.

Within-subject reproducibility varies in multi-modal, longitudinal brain networks

Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.

Acquisition and Analysis of Excised Neocortex from Pediatric Patients with Focal Cortical Dysplasia Using Mesoscale Diffusion MRI.

Non-invasive classification of focal cortical dysplasia (FCD) subtypes remains challenging from a radiology perspective. Quantitative imaging biomarkers (QIBs) have the potential to distinguish subtypes that lack pathognomonic features and might help in defining the extent of abnormal connectivity associated with each FCD subtype. A key motivation of diagnostic imaging is to improve the localization of a "lesion" that can guide the surgical resection of affected tissue, which is thought to cause seizures. Conversely, surgical resections to eliminate or reduce seizures provided unique opportunities to develop magnetic resonance imaging (MRI)-based QIBs by affording long scan times to evaluate multiple contrast mechanisms at the mesoscale (0.5 mm isotropic voxel dimensions). Using ex vivo hybrid diffusion tensor imaging on a 9.4 T MRI scanner, the grey to white matter ratio of scalar indices was lower in the resected middle temporal gyrus (MTG) of two neuropathologically confirmed cases of FCD compared to non-diseased control postmortem fixed temporal lobes. In contrast, fractional anisotropy was increased within FCD and also adjacent white matter tracts. Connectivity (streamlines/mm3) in the MTG was higher in FCD, suggesting that an altered connectivity at the lesion locus can potentially provide a tangible QIB to distinguish and characterize FCD abnormalities. However, as illustrated here, a major challenge for a robust tractographical comparison lies in the considerable differences in the ex vivo processing of bioptic and postmortem samples. Mesoscale diffusion MRI has the potential to better define and characterize epileptic tissues obtained from surgical resection to advance our understanding of disease etiology and treatment.

Multisite harmonization of diffusion tensor image analysis along the perivascular space using the COMBined Association Test

PurposeThis multisite study aimed to use the COMBined Association Test (COMBAT), a harmonization technique that uses regression of covariates with an empirical Bayesian framework, to harmonize diffusion tensor image analysis along the perivascular space (DTI-ALPS) variations caused by scanner, site, and protocol differences.Materials and methodsThis study included multisite diffusion magnetic resonance imaging (dMRI) data of 45 patients with Alzheimer’s disease (AD) and 82 cognitively normal (CN) participants from the AD neuroimaging initiative database. The dMRI data were obtained with two b values (0 and 1000 s/mm2) from 27 institutions and three different 3-Tesla MRI scanners (two vendors). The ALPS index was calculated from multisite dMRI data, and COMBAT was used to harmonize the factors causing site variations. Welch’s t test was used, Cohen’s d was calculated to compare the difference in the ALPS index between AD and CN before and after harmonization, and Pearson’s correlation coefficient was calculated to assess the relationships between the ALPS index and the cognitive score, [18F] fluorodeoxyglucose (FDG)-positron emission tomography (PET), and [18F] florbetapir (AV45)-PET standardized uptake value ratios (SUVRs).ResultsCOMBAT harmonized scanner differences and increased Cohen’s d of the left and right ALPS indexes between AD and CN from 0.288 to 0.438 and 0.328 to 0.480, respectively. The ALPS indexes were significantly different between AD and CN after harmonization (P < 0.05) but not before it. Moreover, Pearson’s correlation coefficients between the ALPS index and cognitive score, FDG-PET, and AV45-PET SUVRs were higher after harmonization than before it.ConclusionThis study demonstrates the application of COMBAT harmonization to eliminate between-scanner, site, and protocol variations in the ALPS index calculated from DTI-ALPS using dMRI and possibly facilitate the use of the ALPS index in multi-center studies.

Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization.

Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview.

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism.

Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines' shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.

An Evaluation of Screening Pathways Using a Combination of Magnetic Resonance Imaging and Prostate-specific Antigen: Results from the IP1-PROSTAGRAM Study

BackgroundThe use of prostate-specific antigen (PSA) testing to screen for prostate cancer has been fraught with under- and overdiagnosis. Short, noncontrast magnetic resonance imaging (MRI) might detect more grade group ≥2 cancers with similar rates of biopsy. ObjectiveTo evaluate strategies that combined PSA and MRI to select men based in the community for a prostate biopsy. Design, setting, and participantsIP1-PROSTAGRAM was a prospective, population-based, paired cohort study of 408 men aged 50–69 yr conducted at seven UK primary care practice and two imaging centres (from October 10, 2018 to May 15, 2019). InterventionAll participants underwent screening with a PSA test, MRI (T2-weighted and diffusion), and transrectal ultrasound (b-mode and elastography). If any test was screen positive, a systematic 12-core biopsy was performed. Additional image-fusion targeted biopsies were taken if the MRI or ultrasound was positive. Outcome measurements and statistical analysisWe conducted an analysis, set out in the statistical plan a priori, comparing 13 different pathways including PSA-alone, MRI-alone, and a range of PSA thresholds and MRI scores. The performance of each pathway was evaluated focusing on the trade-offs between biopsy referral rates and detection of grade group ≥2 cancers. A targeted biopsy was performed only where the PROSTAGRAM MRI showed a lesion score of 3, 4, or 5. Results and limitationsThe standard PSA pathway (PSA ≥3 ng/ml + systematic biopsy) would lead to 10% of men being referred for a biopsy and a 1.0% detection rate of grade group ≥2 cancers. Pathways that relied on MRI alone set at a threshold score of 3 for a biopsy led to higher biopsy rates, but with benefit of high cancer detection rates. The pathway that combined an initial low PSA threshold (≥1.0 ng/ml) and MRI score ≥4 accurately identified a high rate of grade group ≥2 cancers (2.5%, 95% confidence interval 1.3–4.6) while recommending fewer patients for a biopsy (7.1%, 95% confidence interval 4.9–10.2). The results are pertinent to only one screening round, the impact of repeat screening rounds is not evaluated, and the required MRI capacity is currently lacking. ConclusionsOur results highlight the trade-off that exists between reducing excessive numbers of biopsies and maintaining grade group ≥2 cancer detection rates. A pathway that combines PSA ≥1 ng/ml and MRI score ≥4 maintains the detection of grade group ≥2 cancers while recommending fewer men for biopsies and would be the preferred strategy to evaluate in future studies at the first screening round. Patient summaryThe IP1-PROSTAGRAM study shows that PROSTAGRAM magnetic resonance imaging in men with a prostate-specific antigen level of ≥1.0 ng/ml could be a promising pathway to evaluate in future screening trials.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Early path dominance as a principle for neurodevelopment

We perform targeted attack, a systematic computational unlinking of the network, to analyze its effects on global communication across the brain network through its giant cluster. Across diffusion magnetic resonance images from individuals in the UK Biobank, Adolescent Brain Cognitive Development Study and Developing Human Connectome Project, we find that targeted attack procedures on increasing white matter tract lengths and densities are remarkably invariant to aging and disease. Time-reversing the attack computation suggests a mechanism for how brains develop, for which we derive an analytical equation using percolation theory. Based on a close match between theory and experiment, our results demonstrate that tracts are limited to emanate from regions already in the giant cluster and tracts that appear earliest in neurodevelopment are those that become the longest and densest.