False discovery rate control for lesion-symptom mapping with heterogeneous data via weighted p-values

Statistic

False Discovery Rate Control for Lesion-Symptom Mapping with Heterogeneous data via Weighted P-values

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side-information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping (VLSM) studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power

Allocentric neglect strongly associated with egocentric neglect.

To access publisher's full text version of this article. Please click on the hyperlink in Additional Links field.Following brain injury, many patients experience egocentric spatial neglect, where they fail to respond to stimuli on the contralesional side of their body. On the other hand, allocentric, object-based neglect refers to the symptom of ignoring the contralesional side of objects, regardless of the objects' egocentric position. There is an established tradition for considering these two phenomena as both behaviorally and anatomically dissociable. However, several studies and some theoretical work have suggested that these rather reflect two aspects of a unitary underlying disorder. Furthermore, in a recent large study Yue et al. [Archives of Physical Medicine and Rehabilitation 93 (2012) 156] reported that acute allocentric neglect is only observed in cases where substantial egocentric neglect is also present. In a new sample of right hemisphere stroke patients, we attempted to control for potential confounds by using a novel continuous measure for allocentric neglect (in addition to a recently developed continuous measure for egocentric neglect). Our findings suggest a strong association between egocentric and allocentric neglect. Consistent with the work of Yue et al. (2012), we found allocentric behavioral deficits only in conjunction with egocentric deficits as well as a large corresponding overlap for the anatomical regions associated with egocentric and with allocentric neglect. We discuss how different anatomical and behavioral findings can be explained in a unified physiologically plausible framework, whereby allocentric and egocentric effects interact.National Institutes of Health NS054266 DC009571 Deutsche Forschungsgemeinschaft KA 1258/10-1 HA 5839/3-1 PAK 270/

Verifying a Computational Model of Word Production with Voxel-based lesion-parameter Mapping

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Lin4Neuro: a customized Linux distribution ready for neuroimaging analysis

<p>Abstract</p> <p>Background</p> <p>A variety of neuroimaging software packages have been released from various laboratories worldwide, and many researchers use these packages in combination. Though most of these software packages are freely available, some people find them difficult to install and configure because they are mostly based on UNIX-like operating systems. We developed a live USB-bootable Linux package named "Lin4Neuro." This system includes popular neuroimaging analysis tools. The user interface is customized so that even Windows users can use it intuitively.</p> <p>Results</p> <p>The boot time of this system was only around 40 seconds. We performed a benchmark test of inhomogeneity correction on 10 subjects of three-dimensional T1-weighted MRI scans. The processing speed of USB-booted Lin4Neuro was as fast as that of the package installed on the hard disk drive. We also installed Lin4Neuro on a virtualization software package that emulates the Linux environment on a Windows-based operation system. Although the processing speed was slower than that under other conditions, it remained comparable.</p> <p>Conclusions</p> <p>With Lin4Neuro in one's hand, one can access neuroimaging software packages easily, and immediately focus on analyzing data. Lin4Neuro can be a good primer for beginners of neuroimaging analysis or students who are interested in neuroimaging analysis. It also provides a practical means of sharing analysis environments across sites.</p

Brain age predicts long-term recovery in post-stroke aphasia

Funding Information: This study was supported by the following grant sponsors: National Institute on Deafness and Other Communication Disorders (P50 DC014664; DC008355); National Institute of Neurological Disorders and Stroke (NS054266). Publisher Copyright: © 2022 The Author(s).The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.Peer reviewe

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field

Hippocampal Volume Reduction in Congenital Central Hypoventilation Syndrome

Children with congenital central hypoventilation syndrome (CCHS), a genetic disorder characterized by diminished drive to breathe during sleep and impaired CO2 sensitivity, show brain structural and functional changes on magnetic resonance imaging (MRI) scans, with impaired responses in specific hippocampal regions, suggesting localized injury