Open-Label Placebo Injection for Chronic Back Pain With Functional Neuroimaging: A Randomized Clinical Trial.

Importance: Chronic back pain (CBP) is a leading cause of disability. Placebo treatments often provide as much pain relief as bona fide treatments, such as steroid injections. Open-label (honestly prescribed) placebos (OLPs) may relieve CBP without deception, but OLP mechanisms remain poorly understood. Objective: To investigate the long-term efficacy and neurobiological mechanisms of OLP for CBP. Design, Setting, and Participants: A randomized clinical trial of CBP with longitudinal functional magnetic resonance imaging (MRI) comparing OLP with usual care, with 1-year follow-up, was conducted in a university research setting and a community orthopedic clinic. Participants were individuals aged 21 to 70 years with CBP. The trial was conducted from November 2017 to August 2018, with 1-year follow-up completed by November 2019. Data analysis was performed from April 2020 to May 2024. The primary analysis was conducted on an intention-to-treat sample. Interventions: Participants randomized to OLP received a 1-time subcutaneous lumbar saline injection presented as placebo accompanied by information about the power of placebo to relieve pain, alongside their ongoing care. Usual care participants continued their ongoing care. Main Outcomes and Measures: The primary outcome was pain intensity (0-10, with 0 indicating no pain and 10 the most intense) at 1 month posttreatment. Secondary outcomes included pain interference, depression, anxiety, anger, and sleep quality. Functional MRI was performed before and after treatment during evoked and spontaneous back pain. Results: A total of 101 adults (52 [51.4%] females; mean [SD] age, 40.4 [15.4] years) with moderate severity CBP (mean [SD], 4.10 [1.25] intensity; duration, 9.7 [8.5] years) were enrolled. Compared with usual care, OLP reduced CBP intensity posttreatment (relative reduction, 0.61; Hedges g = 0.45; 95% CI, -0.89 to 0.04; P = .02). Through 1-year follow-up, pain relief did not persist, although significant benefits were observed for depression, anger, anxiety, and sleep disruption (Hedges g = 0.3-0.5; all P < .03). Brain responses to evoked back pain for OLP vs usual care increased in rostral anterior cingulate and ventromedial prefrontal cortex and decreased in somatomotor cortices and thalamus. During spontaneous pain, functional connectivity analyses identified OLP vs usual care increases in ventromedial prefrontal cortex connectivity to the rostral ventral medulla, a pain-modulatory brainstem nucleus. No adverse effects of treatment were reported by participants. Conclusions and Relevance: In this randomized clinical trial of OLP vs usual care, a single nondeceptive placebo injection reduced CBP intensity for 1 month posttreatment and provided benefits lasting for at least 1 year posttreatment. Brain mechanisms of OLP in a clinical population overlap with those of deceptive placebos in healthy volunteers, including engagement of prefrontal-brainstem pain modulatory pathways. Trial Registration: ClinicalTrials.gov Identifier: NCT03294148.

Neuroimaging evidence of disturbed self-appraisal in posttraumatic stress disorder: A systematic review.

BACKGROUND: The experience of self-hood in posttraumatic stress disorder (PTSD) is altered cognitively and somatically. Dysfunctional negative cognitions about the self are a central mechanism of PTSD symptomatology and treatment. However, while higher-order brain models of disturbances in self-appraisal (i.e., cognitive processes relating to evaluating the self) have been examined in other psychiatric disorders, it is unclear how normative brain function during self-appraisal is impaired in PTSD. METHODS: This paper presents a PRISMA systematic review of functional neuroimaging studies (n = 5), to establish a neurobiological account of how self-appraisal processes are disturbed in PTSD. The review was prospectively registered with PROSPERO (CRD42023450509). RESULTS: Self-appraisal in PTSD is linked to disrupted activity in core self-processing regions of the Default Mode Network (DMN); and regions involved in cognitive control and emotion regulation, salience and valuation. LIMITATIONS: Because self-appraisal in PTSD is relatively under-studied, only a small number of studies could be included for review. Cross-study heterogeneity in analytic approaches and trauma-exposure history prohibited a quantitative meta-analysis. CONCLUSIONS: This paper proposes a mechanistic account of how neural dysfunctions may manifest clinically in PTSD and inform targeted selection of appropriate treatment options. We present a research agenda for future work to advance the field.

Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury.

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives.

Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Analyzing fractal dimension in electroconvulsive therapy: Unraveling complexity in structural and functional neuroimaging.

BACKGROUND: Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response. METHODS: Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome. RESULTS: Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome. CONCLUSION: This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PST). The first pilot trial showed promising changes in cognitive control measured by functional neuroimaging and improvements in depression and anxiety symptoms. METHODS: To further validate Lumen in a 3-arm randomized clinical trial, 200 participants with mild-to-moderate depression and/or anxiety will be randomly assigned in a 2:1:1 ratio to receive Lumen-coached PST, human-coached PST as active treatment comparison, or a waitlist control condition where participants can receive Lumen after the trial period. Participants will be assessed at baseline and 18 weeks. The primary aim is to confirm neural target engagement by testing whether compared with waitlist controls, Lumen participants will show significantly greater improvements from baseline to 18 weeks in the a priori neural target for cognitive control, right dorsal lateral prefrontal cortex engaged by the go/nogo task (primary superiority hypothesis). A secondary hypothesis will test whether compared with human-coached PST participants, Lumen participants will show equivalent improvements (i.e., noninferiority) in the same neural target from baseline to 18 weeks. The second aim is to examine (1) treatment effects on depression and anxiety symptoms, psychosocial functioning, and quality of life outcomes, and (2) relationships of neural target engagement to these patient-reported outcomes. CONCLUSIONS: This study offers potential to improve the reach and impact of psychotherapy, mitigating access, cost, and stigma barriers for people with depression and/or anxiety. CLINICALTRIALS: gov #: NCT05603923.

Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals.

BACKGROUND: Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. METHOD: Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. RESULT: A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. CONCLUSION: Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.

Disentangling the effects of near-infrared light stimulation and exercise on cognitive function in fNIRS studies.

Functional near-infrared spectroscopy (fNIRS) studies often aim to measure changes in the brain's hemodynamic response in relation to a specific intervention. We recently showed how a fNIRS device could induce photobiomodulatory effects on cognition by using its near-infrared (NIR) light. However, so far, fNIRS research has overlooked the stimulatory potential intrinsic to this technique. The work by Kuwamizu et al. (2023) on pupil dynamics during exercise is no exception. Here, we suggest a fix to their experimental design, which could be taken into account in other fNIRS studies, to guarantee an adequate level of control for possible unconsidered photobiomodulatory effects.

Where alcohol use disorder meets interoception: A meta-analytic view on structural and functional neuroimaging data.

Alcohol use disorder (AUD) has been associated with changes in the processing of internal body signals, known as interoception. Changes in brain structure, particularly in the insula, are thought to underlie impaired interoception. As studies specifically investigating this association are largely lacking, this analysis takes an approach that compares meta-analytic results on interoception with recently published meta-analytic results on gray matter reduction in AUD. A systematic literature search identified 25 eligible interoception studies. Activation likelihood estimation (ALE) was used to test for spatial convergence of study results. Overlap between interoception and AUD clusters was tested using conjunction analysis. Meta-analytic connectivity modeling (MACM) and resting-state functional connectivity were used to identify the functional network of interoception and to test where this network overlapped with AUD meta-analytic clusters. The results were characterized using behavioral domain analysis. The interoception ALE identified a cluster in the left middle insula. There was no overlap with clusters of reduced gray matter in AUD. MACM analysis of the interoception cluster revealed a large network located in the insulae, thalami, basal nuclei, cingulate and medial frontal cortices, and pre- and postcentral gyri. Resting state analysis confirmed this result, showing the strongest connections to nodes of the salience- and somatomotor network. Five of the eight clusters that showed a structural reduction in AUD were located within these networks. The behavioral profiles of these clusters were suggestive of higher-level processes such as salience control, somatomotor functions, and skin sensations. The results suggest an altered salience mapping of interoceptive signals in AUD, consistent with current models. Connections to the somatomotor network may be related to action control and integration of skin sensations. Mindfulness-based interventions, pleasurable touch, and (deep) transcranial magnetic stimulation may be targeted interventions that reduce interoceptive deficits in AUD and thus contribute to drug use reduction and relapse prevention.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation.

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Optimized microburst VNS elicits fMRI responses beyond thalamic-specific response from standard VNS.

OBJECTIVE: In parallel to standard vagus nerve stimulation (VNS), microburst stimulation delivery has been developed. We evaluated the fMRI-related signal changes associated with standard and optimized microburst stimulation in a proof-of-concept study (NCT03446664). METHODS: Twenty-nine drug-resistant epilepsy patients were prospectively implanted with VNS. Three 3T fMRI scans were collected 2 weeks postimplantation. The maximum tolerated VNS intensity was determined prior to each scan starting at 0.125 mA with 0.125 mA increments. FMRI scans were block-design with alternating 30 sec stimulation [ON] and 30 sec no stimulation [OFF]: Scan 1 utilized standard VNS and Scan 3 optimized microburst parameters to determine target settings. Semi-automated on-site fMRI data processing utilized ON-OFF block modeling to determine VNS-related fMRI activation per stimulation setting. Anatomical thalamic mask was used to derive highest mean thalamic t-value for determination of microburst stimulation parameters. Paired t-tests corrected at P < 0.05 examined differences in fMRI responses to each stimulation type. RESULTS: Standard and microburst stimulation intensities at Scans 1 and 3 were similar (P = 0.16). Thalamic fMRI responses were obtained in 28 participants (19 with focal; 9 with generalized seizures). Group activation maps showed standard VNS elicited thalamic activation while optimized microburst VNS showed widespread activation patterns including thalamus. Comparison of stimulation types revealed significantly greater cerebellar, midbrain, and parietal fMRI signal changes in microburst compared to standard VNS. These differences were not associated with seizure responses. INTERPRETATION: While standard and optimized microburst VNS elicited thalamic activation, microburst also engaged other brain regions. Relationship between these fMRI activation patterns and clinical response warrants further investigation. CLINICAL TRIAL REGISTRATION: The study was registered with clinicaltrials.gov (NCT03446664).

Perception of social inclusion/exclusion and response inhibition in adolescents with past suicide attempt: a multidomain task-based fMRI study.

The occurrence of suicidal behaviors increases during adolescence. Hypersensitivity to negative social signals and deficits in cognitive control are putative mechanisms of suicidal behaviors, which necessitate confirmation in youths. Multidomain functional neuroimaging could enhance the identification of patients at suicidal risk beyond standard clinical measures. Three groups of adolescents (N = 96; 78% females, age = 11.6-18.1) were included: patients with depressive disorders and previous suicide attempts (SA, n = 29); patient controls with depressive disorders but without any suicide attempt history (PC, n = 35); and healthy controls (HC, n = 32). We scanned participants with 3T-MRI during social inclusion/exclusion (Cyberball Game) and response inhibition (Go-NoGo) tasks. Neural activation was indexed by the blood-oxygenation-level dependent (BOLD) of the hemodynamic response during three conditions in the Cyberball Game ("Control condition", "Social Inclusion", and "Social Exclusion"), and two conditions in Go-NoGo task ("Go" and "NoGo" blocks). ANCOVA-style analysis identified group effects across three whole-brain contrasts: 1) NoGo vs. Go, 2) Social inclusion vs. control condition, 3) Social exclusion vs. control condition. We found that SA had lower activation in the left insula during social inclusion vs. control condition compared to PC and HC. Moreover, SA compared to PC had higher activity in the right middle prefrontal gyrus during social exclusion vs. control condition, and in bilateral precentral gyri during NoGo vs. Go conditions. Task-related behavioral and self-report measures (Self-reported emotional reactivity in the Cyberball Game, response times and number of errors in the Go-NoGo Task) did not discriminate groups. In conclusion, adolescent suicidal behaviors are likely associated with neural alterations related to the processing of social perception and response inhibition. Further research, involving prospective designs and diverse cohorts of patients, is necessary to explore the potential of neuroimaging as a tool in understanding the emergence and progression of suicidal behaviors.

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

Measurements of the lateral cerebellar hemispheres using near-infrared spectroscopy through comparison between autism spectrum disorder and typical development.

The cerebellum plays a vital role in cognition, communication with the cerebral cortex, and fine motor coordination. Near-infrared spectroscopy (NIRS) is a portable, less restrictive, and noninvasive functional brain imaging method that can capture brain activity during movements by measuring the relative oxyhemoglobin (oxy-Hb) concentrations in the blood. However, the feasibility of using NIRS to measure cerebellar activity requires discussion. We compared NIRS responses between areas assumed to be the cerebellum and the occipital lobe during a fine motor task (tying a bow knot) and a visual task. Our results showed that the oxy-Hb concentration increased more in the occipital lobe than in the cerebellum during the visual task (p =.034). In contrast, during the fine motor task, the oxy-Hb concentration decreased in the occipital lobe but increased significantly in the cerebellum, indicating a notable difference (p =.015). These findings suggest that we successfully captured cerebellar activity associated with processing, particularly fine motor coordination. Moreover, the observed responses did not differ between individuals with autism spectrum disorder and those with typical development. Our study demonstrates the meaningful utility of NIRS as a method for measuring cerebellar activity during movements.

Using in silico perturbational approach to identify critical areas in schizophrenia.

Schizophrenia is a debilitating neuropsychiatric disorder whose underlying correlates remain unclear despite decades of neuroimaging investigation. One contentious topic concerns the role of global signal (GS) fluctuations and how they affect more focal functional changes. Moreover, it has been difficult to pinpoint causal mechanisms of circuit disruption. Here, we analyzed resting-state fMRI data from 47 schizophrenia patients and 118 age-matched healthy controls and used dynamical analyses to investigate how global fluctuations and other functional metastable states are affected by this disorder. We found that brain dynamics in the schizophrenia group were characterized by an increased probability of globally coherent states and reduced recurrence of a substate dominated by coupled activity in the default mode and limbic networks. We then used the in silico perturbation of a whole-brain model to identify critical areas involved in the disease. Perturbing a set of temporo-parietal sensory and associative areas in a model of the healthy brain reproduced global pathological dynamics. Healthy brain dynamics were instead restored by perturbing a set of medial fronto-temporal and cingulate regions in the model of pathology. These results highlight the relevance of GS alterations in schizophrenia and identify a set of vulnerable areas involved in determining a shift in brain state.

Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies.

As the neuroimaging field moves towards detecting smaller effects at higher spatial resolutions, and faster sampling rates, there is increased attention given to the deleterious contribution of unstructured, thermal noise. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, for suppressing thermal noise using datasets acquired with various field strengths, voxel sizes, sampling rates, and task designs. Following minimal preprocessing, statistical activation (t-values) of NORDIC processed data was compared to the results obtained with alternative denoising methods. Additionally, we examined the consistency of the estimates of task responses at the single-voxel, single run level, using a finite impulse response (FIR) model. To examine the potential impact on effective image resolution, the overall smoothness of the data processed with different methods was estimated. Finally, to determine if NORDIC alters or removes temporal information important for modeling responses, we employed an exhaustive leave-p-out cross validation approach, using FIR task responses to predict held out timeseries, quantified using R2. After NORDIC, the t-values are increased, an improvement comparable to what could be achieved by 1.5 voxels smoothing, and task events are clearly visible and have less cross-run error. These advantages are achieved with smoothness estimates increasing by less than 4%, while 1.5 voxel smoothing is associated with increases of over 140%. Cross-validated R2s based on the FIR models show that NORDIC is not measurably distorting the temporal structure of the data under this approach and is the best predictor of non-denoised time courses. The results demonstrate that analyzing 1 run of data after NORDIC produces results equivalent to using 2 to 3 original runs and that NORDIC performs equally well across a diverse array of functional imaging protocols. Significance Statement: For functional neuroimaging, the increasing availability of higher field strengths and ever higher spatiotemporal resolutions has led to concomitant increase in concerns about the deleterious effects of thermal noise. Historically this noise source was suppressed using methods that reduce spatial precision such as image blurring or averaging over a large number of trials or sessions, which necessitates large data collection efforts. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, which suppresses thermal noise. Across datasets varying in field strength, voxel sizes, sampling rates, and task designs, NORDIC produces substantial gains in data quality. Both conventional t-statistics derived from general linear models and coefficients of determination for predicting unseen data are improved. These gains match or even exceed those associated with 1 voxel Full Width Half Max image smoothing, however, even such small amounts of smoothing are associated with a 52% reduction in estimates of spatial precision, whereas the measurable difference in spatial precision is less than 4% following NORDIC.

Development of top-down cortical propagations in youth.

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

Review of Applications of Near-Infrared Spectroscopy in Two Rare Disorders with Executive and Neurological Dysfunction: UCD and PKU.

Studying rare diseases, particularly those with neurological dysfunction, is a challenge to researchers and healthcare professionals due to their complexity and small population with geographical dispersion. Universal and standardized biomarkers generated by tools such as functional neuroimaging have been forged to collect baseline data as well as treatment effects. However, the cost and heavily infrastructural requirement of those technologies have substantially limited their availability. Thus, developing non-invasive, portable, and inexpensive modalities has become a major focus for both researchers and clinicians. When considering neurological disorders and diseases with executive dysfunction, EEG is the most convenient tool to obtain biomarkers which can correlate the objective severity and clinical observation of these conditions. However, studies have also shown that EEG biomarkers and clinical observations alone are not sensitive enough since not all the patients present classical phenotypical features or EEG evidence of dysfunction. This article reviews disorders, including two rare disorders with neurological dysfunction and the usefulness of functional near-infrared spectroscopy (fNIRS) as a non-invasive optical modality to obtain hemodynamic biomarkers of diseases and for screening and monitoring the disease.