Tag: nevin manimala

Imaging Neurosci (Camb). 2024 Jun 24;2:imag-2-00198. doi: 10.1162/imag_a_00198. eCollection 2024.

ABSTRACT

Two permutation-based methods for simultaneous inference on the proportion of active voxels in cluster-wise brain imaging analysis have recently been published: Notip and pARI. Both rely on the definition of a critical vector of ordered $p$ -values, chosen from a family of candidate vectors, but differ in how the family is defined: computed from randomization of external data for Notip and determined a priori for pARI. These procedures were compared to other proposals in the literature, but an extensive comparison between the two methods is missing due to their parallel publication. We provide such a comparison and find that pARI outperforms Notip if both methods are applied under their recommended settings. However, each method carries different advantages and drawbacks.

PMID:40800516 | PMC:PMC12272252 | DOI:10.1162/imag_a_00198

Imaging Neurosci (Camb). 2024 Oct 30;2:imag-2-00346. doi: 10.1162/imag_a_00346. eCollection 2024.

ABSTRACT

The flow of functional connectivity (FC) is thought to be supported by white matter structural connectivity (SC). While research on the correlations between SC and FC (SC-FC coupling) has progressed, the genetic implications of SC-FC coupling have not been thoroughly examined. Traditionally, SC-FC coupling investigations utilize predefined atlases. Here, we adopted an atlas-free SC-FC coupling built on the high-resolution white surface (the interface of white matter and gray matter) to uncover common genetic variations. Leveraging data from the Human Connectome Project, we demonstrated considerable heritability in areas within the early and intermediate visual cortex and across dorsal-attention, language, and somatomotor functional networks. We detected 334 genetic loci (spanning 234 cytogenetic bands) linked to SC-FC coupling (P < 1.26 × 10^-11), notably in cingulo-opercular, somatomotor, and default mode networks. Using an external dataset from the Adolescent Brain Cognitive Development study, we confirmed 187 cytogenetic bands associated with SC-FC coupling across 22 brain regions (P < 1 × 10^-5). Genetic correlation analyses revealed high genetic interrelatedness for SC-FC coupling in neighboring regions. Furthermore, it showed genetic correlations with a spectrum of complex traits, encompassing various neurological and psychiatric conditions. In essence, our study paves the way towards deciphering the genetic interplay between structural and functional connectivity of the brain.

PMID:40800503 | PMC:PMC12290780 | DOI:10.1162/imag_a_00346

Imaging Neurosci (Camb). 2024 Dec 13;2:imag-2-00388. doi: 10.1162/imag_a_00388. eCollection 2024.

ABSTRACT

Pooling multi-site datasets is the dominant trend to expand sample sizes in neuroimaging field, thereby enhancing statistical power and reproducibility of research findings. Nevertheless, the heterogeneity derived from aggregating data from various imaging sites obstructs efficient inferences. Our recent study thoroughly assessed methods for harmonizing multi-site resting-state fMRI images, accelerating progress and providing initial application instructions. Despite this advancement, the removal of such site effects generally necessitates a certain level of programming expertise. In our effort to streamline the harmonization of site effects using advanced methodologies, we are pleased to introduce the DPABI Harmonization module. This versatile tool, allowing agnostic to specific analysis methods, integrates a range of techniques, including the state-of-the-art Subsampling Maximum-mean-distance Algorithms (SMA, recommended), ComBat/CovBat, linear models, and invariant conditional variational auto-encoder (ICVAE). It equips neuroscientists with an easy-to-use and transparent harmonization workflow, ensuring the feasibility of post-hoc analysis for multi-site studies.

PMID:40800502 | PMC:PMC12315742 | DOI:10.1162/imag_a_00388

Imaging Neurosci (Camb). 2024 May 20;2:imag-2-00169. doi: 10.1162/imag_a_00169. eCollection 2024.

ABSTRACT

Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network (“DMN-C”) that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant’s whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function.

PMID:40800501 | PMC:PMC12247584 | DOI:10.1162/imag_a_00169

Imaging Neurosci (Camb). 2024 Feb 2;2:imag-2-00082. doi: 10.1162/imag_a_00082. eCollection 2024.

ABSTRACT

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.

PMID:40800496 | PMC:PMC12224406 | DOI:10.1162/imag_a_00082

Imaging Neurosci (Camb). 2024 Jul 8;2:imag-2-00201. doi: 10.1162/imag_a_00201. eCollection 2024.

ABSTRACT

The default mode (DMN), frontoparietal (FPN), and salience (SN) networks interact to support a range of behaviors, are vulnerable to environmental insults, and are disrupted in neurodevelopmental disorders. However, their development across the third trimester and perinatal transition remains unknown. Employing resting-state functional MRI at 30 to 32, 34 to 36, and 40 to 44 weeks postmenstrual age (PMA), we examined developmental trajectories of the intra- and internetwork connectivity of the 3 networks measured in 84 fetuses and neonates. A secondary analysis addressed the impact of maternal mental health on these networks. The DMN, FPN, and SN intranetwork connectivity evidenced significant increases between 36 and 44 weeks PMA, with connectivity measures reaching values significantly greater than 0 at 40 weeks PMA for all 3 networks. Connectivity between SN and DMN and between SN and FPN decreased significantly with the connectivity values significantly below 0 at 36-44 weeks. However, DMN-FPN connectivity increased between 30 and 44 weeks with the connectivity greater than 0 already at 36 months. Finally, higher maternal stress levels negatively affected the SN across 30-44 weeks PMA. These data provide a normative framework to compare fetuses and neonates at risk for neurobehavioral disorders and assess the impact of the environment on the developing brain.

PMID:40800486 | PMC:PMC12272234 | DOI:10.1162/imag_a_00201

Imaging Neurosci (Camb). 2024 Jul 24;2:imag-2-00241. doi: 10.1162/imag_a_00241. eCollection 2024.

ABSTRACT

In this perspective, we highlight how emerging artificial intelligence tools are likely to impact the experiences of researchers conducting computational fMRI analyses. While calls for the automatization of statistical procedures date back at least to the inception of “data science” as a field, generative artificial intelligence offers new opportunities to advance field practice. We highlight how these tools are poised to impact both new neuroimaging methods development in areas such as image quality control and in day-to-day practice when generating analysis code. We argue that considering generative artificial intelligence as a catalyst for computational neuroscience-rather than as unique tools in their own right-can substantially improve its positioning in the research ecosystem. In particular, we argue that generative artificial intelligence will reinforce the importance of existing open science initiatives, rather than supplanting them. Overall, we call for clearer metrics by which neuroimaging results-whether generated by individual research teams or by generative artificial intelligence technologies-can be meaningfully compared.

PMID:40800478 | PMC:PMC12272269 | DOI:10.1162/imag_a_00241

Imaging Neurosci (Camb). 2024 Nov 8;2:imag-2-00358. doi: 10.1162/imag_a_00358. eCollection 2024.

ABSTRACT

Meta-analytic contrasts are a promising aspect of coordinate-based meta-analyses in neuroimaging research as they facilitate the statistical comparison of two meta-analytic results. They have been used for a multitude of comparisons, such as task conditions, cognitive processes, and groups. However, it remains to be tested how the results of meta-analytic contrasts relate to those of classic meta-analyses and vice versa. Here, we present a comprehensive empirical investigation of this issue using four datasets from different cognitive domains: working memory, working memory load, cognitive interference processing, and emotional face processing. For all four datasets, we compared the results of a standard meta-analysis across prototypical contrasts (condition A > condition B) reported in individual experiments with those of a contrast between two individual meta-analyses of the same conditions (meta-analysis condition A > meta-analysis condition B). In the meta-analytic contrasts, similar brain regions as in the standard meta-analysis were found but with relatively distinct spatial activation patterns. Additionally, fewer regions were revealed in the meta-analytic contrasts, especially in areas where the conditions spatially overlapped. This can be ascribed to the loss of information on the strength of activations in meta-analytic contrasts, across which standard meta-analysis summarize. In one dataset, additional regions were found in the meta-analytic contrast, potentially due to task effects. Our results demonstrate that meta-analytic contrasts can yield similar results to standard meta-analyses but are sparser. This confirms the overall validity, but also limited ability to capture all regions found in standard meta-analyses. Notable differences observed in some cases indicate that such contrasts cannot be taken as an easy substitute for classic meta-analyses of experiment-level contrasts, warranting further research into the boundary conditions for agreement.

PMID:40800475 | PMC:PMC12315735 | DOI:10.1162/imag_a_00358

Imaging Neurosci (Camb). 2024 May 20;2:imag-2-00175. doi: 10.1162/imag_a_00175. eCollection 2024.

ABSTRACT

Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal cortical thickness). Recent advances in scan acceleration enable extremely fast T₁-weighted scans (~1 minute) that achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as “cluster scanning.” Here, we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer’s Dementia). Morphometric errors from a single compressed sensing (CS) 1.0 mm scan (CS) were, on average, 12% larger than a traditional scan using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CS acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CS scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scans of different spatial resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.

PMID:40800474 | PMC:PMC12247576 | DOI:10.1162/imag_a_00175

Imaging Neurosci (Camb). 2024 Nov 5;2:imag-2-00345. doi: 10.1162/imag_a_00345. eCollection 2024.

ABSTRACT

The fields of neuroscience and psychology are currently in the midst of aso-called reproducibility crisis, with growing concerns regarding a history ofweak effect sizes and low statistical power in much of the research published inthese fields over the last few decades. Whilst the traditional approach foraddressing this criticism has been to increaseparticipantsample sizes, there are many research contexts in which the number oftrialsper participant may be of equal importance. Thepresent study aimed to compare the relative importance of participants andtrials in the detection of phase-dependent phenomena, which are measured acrossa range of neuroscientific contexts (e.g., neural oscillations, non-invasivebrain stimulation). This was achievable within a simulated environment where onecan manipulate the strength of this phase dependency in two types of outcomevariables: one with normally distributed residuals (idealistic) and onecomparable with motor-evoked potentials (an MEP-like variable). We compared thestatistical power across thousands of experiments with the same number ofsessions per experiment but with different proportions of participants andnumber of sessions per participant (30 participants × 1 session, 15participants × 2 sessions, and 10 participants × 3 sessions), withthe trials being pooled across sessions for each participant. These simulationswere performed for both outcome variables (idealistic and MEP-like) and fourdifferent effect sizes (0.075-“weak,”0.1-“moderate,” 0.125-“strong,”0.15-“very strong”), as well as separate control scenarioswith no true effect. Across all scenarios with (true) discoverable effects, andfor both outcome types, there was a statistical benefit for experimentsmaximising the number of trials rather than the number of participants (i.e., itwas always beneficial to recruit fewer participants but have them complete moretrials). These findings emphasise the importance of obtaining sufficientindividual-level data rather than simply increasing number of participants.

PMID:40800470 | PMC:PMC12290742 | DOI:10.1162/imag_a_00345