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Recording human brain activity is crucial for understanding normal and aberrant brain function. However, available recording methods are either highly invasive or have relatively low sensitivity. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging. However, fUSI cannot be performed through adult human skull. Here, we use a polymeric skull replacement material to create an acoustic window allowing ultrasound to monitor brain activity in fully intact adult humans. We design the window through experiments in phantoms and rodents, then implement it in a participant undergoing reconstructive skull surgery. Subsequently, we demonstrate fully non-invasive mapping and decoding of cortical responses to finger movement, marking the first instance of high-resolution (200 μm) and large-scale (50 mmx38 mm) brain imaging through a permanent acoustic window.

Cerebral small vessels are largely inaccessible to existing clinical in vivo imaging technologies. This study aims to present a novel analysis pipeline for vessel density mapping of cerebral small vessels from high-resolution 3D black-blood MRI at 3T. Twenty-eight subjects (10 under 35 years old, 18 over 60 years old) were imaged with the T1-weighted turbo spin-echo with variable flip angles (T1w TSE-VFA) sequence optimized for black-blood small vessel imaging with iso-0.5mm spatial resolution at 3T. Hessian-based vessel segmentation methods (Jerman, Frangi and Sato filter) were evaluated by vessel landmarks and manual annotation of lenticulostriate arteries (LSAs). Using optimized vessel segmentation, large vessel pruning and non-linear registration, a semiautomatic pipeline was proposed for quantification of small vessel density across brain regions and further for localized detection of small vessel changes across populations. Voxel-level statistics was performed to compare vessel density between two age groups. Additionally, local vessel density of aged subjects was correlated with their corresponding gross cognitive and executive function (EF) scores using Montreal Cognitive Assessment (MoCA) and EF composite scores compiled with Item Response Theory (IRT). Jerman filter showed better performance for vessel segmentation than Frangi and Sato filter which was employed in our pipeline. Cerebral small vessels on the order of a few hundred microns can be delineated using the proposed analysis pipeline on 3D black-blood MRI at 3T. The mean vessel density across brain regions was significantly higher in young subjects compared to aged subjects. In the aged subjects, localized vessel density was positively correlated with MoCA and IRT EF scores. The proposed pipeline is able to segment, quantify, and detect localized differences in vessel density of cerebral small vessels based on 3D high-resolution black-blood MRI. This framework may serve as a tool for localized detection of small vessel density changes in normal aging and cerebral small vessel disease.

Herein we report the first in-human transcranial imaging of brain function using photoacoustic computed tomography. Functional responses to benchmark motor tasks were imaged on both the skull-less and the skull-intact hemispheres of a hemicraniectomy patient. The observed brain responses in these preliminary results demonstrate the potential of photoacoustic computed tomography for achieving transcranial functional imaging.

IntroductionTo describe the protocol and findings of the instrumental validation of three imaging-based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1-weighted imaging.MethodsThe instrumental validation of imaging-based biomarker kits included inter-rater reliability among participating sites, test-retest repeatability, and inter-scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra-class correlation coefficients (ICC).ResultsThe three biomarkers demonstrated excellent inter-rater reliability (ICC>0.94, P-values0.98, P-values0.98, P-values&nbsp

Introduction: Alzheimer’s disease (AD) has been characterized as a disconnection-syndrome (Villain, 2010). Depending on the disease severity, atrophy in grey matter and disruptions in white matter (WM) are observed. These structural changes are accompanied by functional decline on a symptomatic level, and in intrinsic functional connectivity (FC) of brain networks. In AD, FC alterations are found in several resting-state networks (RSN) such as default mode network (DMN), salience network (SAL) or executive control networks (ECNs). These RSNs can be characterized by their properties of region to region static FC across the duration of an MRI BOLD scan, the variability of the dynamic modulations of this FC (dynFC). In addition to the relation between signal fluctuations between brain areas, the regularity/complexity of BOLD signal fluctuations can be assessed by means of multi-scale entropy (MSE; Smith, 2014) and has been shown to be related to cognitive function in elderly controls (Yang, 2013). Hence, we investigated alterations of dynFC between and MSE of signals within brain regions of these RSNs in early AD patients as compared to elderly controls (EC). Furthermore, we were interested in the relationship of these network characteristics and the MMSE score. Methods: Data from 14 EC (age=67.9, SD=3.7; MMSE=28.6, SD=0.8) and 16 patients with AD (age=67.3, SD=9.7; MMSE=25.2, SD=3.6; Mann-Whitney UEC-AD=19.5, p

© 2015 Wiley Periodicals, Inc. One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI (rs-fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here, we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of "lagged" subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an "objective" estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet-based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals.

Auditory hallucinations (AH) occur in various neurological and psychiatric disorders. In psychosis, increased neuronal activity in the primary auditory cortex (PAC) contributes to AH. We investigated functional neuroanatomy of epileptic hallucinations by measuring cerebral perfusion in three patients with AH during simple partial status epilepticus. Hyperperfusion in the temporal lobe covering the PAC occurred in all patients. Our perfusion data support the hypothesis of PAC being a constituting element in the genesis of AH independent of their aetiology.