Biology subjects

Haber, S.

Publications and source records attributed to Haber, S..

4 recordsLinked to original sources

bioRxiv2024

Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training

BackgroundfMRI neurofeedback targeting the amygdala is a promising therapeutical tool in psychiatry. It induces resting-state functional connectivity (rsFC) changes between the amygdala and regions of the salience and default mode networks (SN and DMN, respectively). We hypothesize these rsFC changes happen on the amygdalas underlying anatomical circuits. MethodsWe used the coordinates from regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala. Using a cross-species brain parcellation, we identified the homologous locations in non-human primates. We injected bidirectional tracers in the amygdala of adult macaques and used bright- and dark-field microscopy to identify cells and axon terminals in each ROI. We also performed additional injections in specific ROIs to validate the results following amygdala injections and delineate potential disynaptic pathways. Finally, we used high-resolution diffusion MRI data from four post-mortem macaque brains and one in vivo human brain to translate our findings to the neuroimaging domain. ResultsThe amygdala had significant monosynaptic connections with all the SN and DMN ipsilateral ROIs. Amygdala connections with the DMN contralateral ROIs are disynaptic through the hippocampus and parahippocampal gyrus. Diffusion MRI in both species benefitted from using the ground-truth tracer data to validate its findings, as we identified false-negative ipsilateral and false-positive contralateral connectivity results. ConclusionsAmygdala neurofeedback modulates the SN and DMN through monosynaptic connections and disynaptic pathways - including hippocampal structures involved in the neurofeedback task. Neurofeedback may be a tool for rapid modulation and reinforcement of these anatomical connections, leading to clinical improvement.

neuroscience↗

bioRxiv2021

Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from todays state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

neuroscience↗

bioRxiv2020

Empty sella syndrome as a window into the neuroprotective effects of prolactin

BACKGROUNDTo correlate structural integrity of visual pathway white matter tracts with prolactin levels in a patient who demonstrates downward herniation of the optic chiasm secondary to medical treatment of a prolactinoma. METHODSA 36-year-old woman with a prolactinoma presented with progressive bilateral visual field defects nine years after initial diagnosis and medical treatment. She was diagnosed with empty-sella syndrome and instructed to stop cabergoline. Hormone testing was conducted in tandem with routine clinical evaluations over one year and the patient was followed with diffusion magnetic resonance imaging (dMRI), optical coherence tomography (OCT), and automated perimetry at three time points. Five healthy controls underwent a complementary battery of clinical and neuroimaging tests at a single time point. RESULTSShortly after discontinuing cabergoline, diffusion metrics in the optic tracts were within the range of values observed in healthy controls. However, following a brief period where the patient resumed cabergoline (of her own volition), there was a decrease in serum prolactin with a corresponding decrease in visual ability and increase in radial diffusivity (p<0.001). Those measures again returned to their baseline ranges after discontinuing cabergoline a second time. CONCLUSIONSThese results demonstrate the sensitivity of dMRI to detect rapid and functionally significant microstructural changes in white matter tracts secondary to alterations in serum prolactin levels. The inverse relations between prolactin and measures of diffusion and visual function provide support for a neuroprotective role of prolactin in the injured nervous system.

neuroscience↗

bioRxiv2020

Modelling white matter in gyral blades as a continuous vector field

1Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called "gyral biases" limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemispheric connections when compared to tracers.

neuroscience↗