Nevin Manimala

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

Imaging Neurosci (Camb). 2024 Jan 9;2:imag-2-00058. doi: 10.1162/imag_a_00058. eCollection 2024.

ABSTRACT

Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-functional magnetic resonance imaging (fMRI) data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.

PMID:40800456 | PMC:PMC12224426 | DOI:10.1162/imag_a_00058

Imaging Neurosci (Camb). 2024 Oct 3;2:imag-2-00306. doi: 10.1162/imag_a_00306. eCollection 2024.

ABSTRACT

T1-weighted imaging holds wide applications in clinical and research settings; however, the challenge of inter-scanner variability arises when combining data across scanners, which impedes multi-site research. To address this, post-acquisition harmonization methods such as statistical or deep learning approaches have been proposed to unify cross-scanner images. Nevertheless, how inter-scanner variability manifests in images and derived measures, and how to harmonize it in an interpretable manner, remains underexplored. To broaden our knowledge of inter-scanner variability and leverage it to develop a new harmonization strategy, we devised a pipeline to assess the interpretable inter-scanner variability in matched T1-weighted images across four 3T MRI scanners. The pipeline incorporates ComBat modeling with 3D superpixel parcellation algorithm (namely SP-ComBat), which estimates location and scale effects to quantify the shift and spread in relative signal distributions, respectively, concerning brain tissues in the image domain. The estimated parametric maps revealed significant contrast deviations compared to the joint signal distribution across scanners (p< 0.001), and the identified deviations in signal intensities may relate to differences in the inversion time acquisition parameter. To reduce the inter-scanner variability, we implemented a harmonization strategy involving proper image preprocessing and site effect removal by ComBat-derived parameters, achieving substantial improvement in image quality and significant reduction in variation of volumetric measures of brain tissues (p< 0.001). We also applied SP-ComBat to evaluate and characterize the performance of various image harmonization techniques, demonstrating a new way to assess image harmonization. In addition, we reported various metrics of T1-weighted images to quantify the impact of inter-scanner variation, including signal-to-noise ratio, contrast-to-noise ratio, signal inhomogeneity index, and structural similarity index. This study demonstrates a pipeline that extends the implementation of statistical ComBat method to the image domain in a practical manner for characterizing and harmonizing the inter-scanner variability in T1-weighted images, providing further insight for the studies focusing on the development of image harmonization methodologies and their applications.

PMID:40800451 | PMC:PMC12290534 | DOI:10.1162/imag_a_00306

Imaging Neurosci (Camb). 2024 Oct 25;2:imag-2-00328. doi: 10.1162/imag_a_00328. eCollection 2024.

ABSTRACT

The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl- ${}_{}{}^{11}\text{C}$ ]WAY100635; however, the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma-free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [ ${}_{}{}^{11}\text{C}$ ]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [ ${}_{}{}^{11}\text{C}$ ]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over-expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches, therefore, demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

PMID:40800447 | PMC:PMC12290790 | DOI:10.1162/imag_a_00328