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•We find that the cardiac hemodynamic phase function is time shifted locally.•We find that the respiratory hemodynamic phase function has single form across the brain.•We propose automatic physiologic signal detection without the external physiologic signal measures and its correction method in resting state-fMRI data.•We compare the efficacy of the proposed method to RETROICOR. Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR. [Display omitted]

•An in-vivo high-resolution χ-separation method at 7T using a deep neural network is proposed.•The method is validated against 3T χ-separation maps and outperforms alternative pipelines.•The method effectively delineates detailed brain structures related to iron and myelin distribution. A recently introduced quantitative susceptibility mapping (QSM) technique, χ-separation, offers the capability to separate paramagnetic (χpara) and diamagnetic (χdia) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by χ-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying χ-separation at 7T presents difficulties due to the requirement of an R2 map, coupled with issues such as high specific absorption rate (SAR), large B1 transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2′ map. Building on this development, we present a new pipeline for χ-separation at 7T, enabling us to generate high-resolution χ-separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scaled R2′, and is validated against χ-separation maps at 3T, demonstrating its accuracy. The 7T χ-separation maps generated by the proposed method exhibit similar contrasts to those from 3T, while 7T high-resolution maps offer enhanced clarity and detail. Quantitative analysis confirms that the proposed method surpasses the alternative pipelines. The proposed method results well delineate the detailed brain structures associated with iron and myelin. This new pipeline holds promise for analyzing iron and myelin concentration changes in various neurodegenerative diseases through precise structural examination.

Cognitive impairment is a common symptom in individuals with Multiple Sclerosis (MS), but meaningful, reliable biomarkers relating to cognitive decline have been elusive, making evaluation of the impact of therapeutics on cognitive function difficult. Here, we combine pathway-based MRI measures of structural and functional connectivity to construct a metric of functional decline in MS. The Structural and Functional Connectivity Index (SFCI) is proposed as a simple, z-scored metric of structural and functional connectivity, where changes in the metric have a simple statistical interpretation and may be suitable for use in clinical trials. Using data collected at six time points from a 2-year longitudinal study of 20 participants with MS and 9 age- and sex-matched healthy controls, we probe two common symptomatic domains, motor and cognitive function, by measuring structural and functional connectivity in the transcallosal motor pathway and posterior cingulum bundle. The SFCI is significantly lower in participants with MS compared to controls ( p = 0.009) and shows a significant decrease over time in MS ( p = 0.012). The change in SFCI over two years performed favorably compared to measures of brain parenchymal fraction and lesion volume, relating to follow-up measures of processing speed (r = 0.60, p = 0.005), verbal fluency (r = 0.57, p = 0.009), and score on the Multiple Sclerosis Functional Composite (r = 0.67, p = 0.003). These initial results show that the SFCI is a suitable metric for longitudinal evaluation of functional decline in MS.

In echo-planar imaging (EPI), such as commonly used for functional MRI (fMRI) and diffusion-tensor imaging (DTI), compressed distortion is a more difficult challenge than local stretching as spatial information can be lost in strongly compressed areas. In addition, the effects are more severe at ultra-high field (UHF) such as 7T due to increased field inhomogeneity. To resolve this problem, two EPIs with opposite phase-encoding (PE) polarity were acquired and combined after distortion correction. For distortion correction, a point spread function (PSF) mapping method was chosen due to its high correction accuracy and extended to perform distortion correction of both EPIs with opposite PE polarity thus reducing the PSF reference scan time. Because the amount of spatial information differs between the opposite PE datasets, the method was further extended to incorporate a weighted combination of the two distortion-corrected images to maximize the spatial information content of a final corrected image. The correction accuracy of the proposed method was evaluated in distortion-corrected data using both forward and reverse phase-encoded PSF reference data and compared with the reversed gradient approaches suggested previously. Further we demonstrate that the extended PSF method with an improved weighted combination can recover local distortions and spatial information loss and be applied successfully not only to spin-echo EPI, but also to gradient-echo EPIs acquired with both PE directions to perform geometrically accurate image reconstruction.

Background: Imaging can provide noninvasive neural markers of disease progression in multiple sclerosis (MS) that are related to behavioral and cognitive symptoms. Past work suggests that diffusion tensor imaging (DTI) provides a measure of white matter pathology, including demyelination and axonal counts. Objectives: In the current study, the authors investigate the relationship of DTI measures in the cingulum bundle to common deficits in MS, including episodic memory, working memory, and information processing speed. Methods: Fifty-seven patients with MS and 17 age- and education-matched controls underwent high-spatial resolution diffusion scans and cognitive testing. Probabilistic tracking was used to generate tracks from the posterior cingulate cortex to the entorhinal cortex. Results: Radial and axial diffusivity values were significantly different between patients and controls (p < 0.031), and in patients bilateral diffusion measures were significantly related to measures of episodic memory and speed of processing (p < 0.033). Conclusions: The tractography-based measures of posterior cingulum integrity reported here support further development of DTI as a viable measure of axonal integrity and cognitive function in patients with MS.

Objectives: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. Methods: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. Results: This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. Conclusions: The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome. (JINS, 2016, 22, 105–119)

Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression. •We present a pulse sequence to acquire BOLD data with known motion corruption.•BOLD data with induced motion is acquired in cadavers and live subjects at rest.•We shows contemporary motion measures are insensitive to intravolume motion.•We present a retrospective algorithm to obtain full slicewise motion estimates.•SLOMOCO is the first motion correction suitable for realistic head motion.

The pain matrix is comprised of an extensive network of brain structures involved in sensory and/or affective information processing. The thalamus is a key structure constituting the pain matrix. The thalamus serves as a relay center receiving information from multiple ascending pathways and relating information to and from multiple cortical areas. However, it is unknown how thalamocortical networks specific to sensory-affective information processing are functionally integrated. Here, in a proof-of-concept study in healthy humans, we aimed to understand this connectivity using transcranial direct current stimulation (tDCS) targeting primary motor (M1) or dorsolateral prefrontal cortices (DLPFC). We compared changes in functional connectivity (FC) with DLPFC tDCS to changes in FC with M1 tDCS. FC changes were also compared to further investigate its relation with individual's baseline experience of pain. We hypothesized that resting-state FC would change based on tDCS location and would represent known thalamocortical networks. Ten right-handed individuals received a single application of anodal tDCS (1 mA, 20 min) to right M1 and DLPFC in a single-blind, sham-controlled crossover study. FC changes were studied between ventroposterolateral (VPL), the sensory nucleus of thalamus, and cortical areas involved in sensory information processing and between medial dorsal (MD), the affective nucleus, and cortical areas involved in affective information processing. Individual's perception of pain at baseline was assessed using cutaneous heat pain stimuli. We found that anodal M1 tDCS and anodal DLPFC tDCS both increased FC between VPL and sensorimotor cortices, although FC effects were greater with M1 tDCS. Similarly, anodal M1 tDCS and anodal DLPFC tDCS both increased FC between MD and motor cortices, but only DLPFC tDCS modulated FC between MD and affective cortices, like DLPFC. Our findings suggest that M1 stimulation primarily modulates FC of sensory networks, whereas DLPFC stimulation modulates FC of both sensory and affective networks. Our findings when replicated in a larger group of individuals could provide useful evidence that may inform future studies on pain to differentiate between effects of M1 and DLPFC stimulation. Notably, our finding that individuals with high baseline pain thresholds experience greater FC changes with DLPFC tDCS implies the role of DLPFC in pain modulation, particularly pain tolerance.

Inflammation and altered neurovascular dysfunction have been noted as features of Alzheimer's Disease (AD) pathophysiology . In this context understanding nature and degree of disruption of brain blood barrier (BBB) is important in AD studies , In this regard, brain region specific changes in well characterized AD subjects at distinct clinical stages of AD is yet to be well characterized. We therefore aimed to investigate the brain regional changes in BBB permeability in different clinical stages of AD versus healthy controls (HC). 41 subjects (70.2 ± 3.5 YO, F=26) were scanned at 3T under IRB consent. There were 6 AD dementia, 16 mild cognitive impairment from AD (MCI) and 19 HC based on clinical dementia rating scale, Montreal Cognitive assessment (MOCA) and positive AD biomarkers. Dynamic contrast enhanced (DCE) imaging was acquired with 3d GRE scans. Permeability (Ktrans) values were calculated with Patlak model in subcortical gray matter (GM) and GM lobe parcellation . We found the significant differences of averaged Ktrans values reflecting larger BBB permeability in thalamus, caudate, putamen, amygdala, hippocampus and occipital regions in AD than HC group. (see Table 1 and Figure 1). We find that the significantly negative correlation between Ktrans and MOCA scores (p < 0.05), as shown in Figure 2. We find that increased permeability in specific sub-/cortical GM regions were more prominent in AD dementia than MCI and was significantly different from HC. These changes also linearly tracked severity of cognitive decline. References: 1. Jellinger KA. J Neural Transm (Vienna). 2020; 2. Knopman DS, et al. J Neuropathol Exp Neurol. 2003; 3. Price JL, et al. Neurobiol Aging. 2009; 4. Fiala M, et al. Eur J Clin Invest. 2002; 5. Pillai JA, et al. Ann Clin Transl Neurol. 2019; 6. Sweeney MD, et al. Nat Rev Neurol. 2018; 7. Patlak CS, et al. J Cereb Blood Flow Metab. 1983; 8. Fischl B. Neuroimage. 2012; 9. Montagne A, et al. Neuron. 2015; 10. Nation DA, et al. Nature Medicine. 2019; 11. Sweeney MD, et al. Alzheimers Dement. 2019; 12. van de Haar HJ, et al. Radiology. 2016.

Cross-correlation of low-frequency temporal fluctuations (<0.08 Hz) was used to correlate widely separated anatomic regions during continuous performance of a spatial working memory task. The regions of highest correlation to right-hemisphere dorsolateral prefrontal cortex correspond to the regions of largest baseline signal change in a conventional block-style functional MRI paradigm. Additionally, it is shown that the correlations between elements of the functional network increase during performance of a task that activates the network when compared to a task that does not directly stimulate the functionally connected network.