Advances In Functional Magnetic Resonance Imaging Research Articles

Streams of conscious visual experience

Consciousness, a cornerstone of human cognition, is believed to arise from complex neural interactions. Traditional views have focused on localized fronto-parietal networks or broader inter-regional dynamics. In our study, we leverage advanced fMRI techniques, including the novel Functionnectome framework, to unravel the intricate relationship between brain circuits and functional activity shaping visual consciousness. Our findings underscore the importance of the superior longitudinal fasciculus within the fronto-parietal fibers, linking conscious perception with spatial neglect. Additionally, our data reveal the critical contribution of the temporo-parietal fibers and the splenium of the corpus callosum in connecting visual information with conscious representation and their verbalization. Central to these networks is the thalamus, posited as a conductor in synchronizing these interactive processes. Contrasting traditional fMRI analyses with the Functionnectome approach, our results emphasize the important explanatory power of interactive mechanisms over localized activations for visual consciousness. This research paves the way for a comprehensive understanding of consciousness, highlighting the complex network of neural connections that lead to awareness.

Task and stimulus coding in the multiple-demand network.

In the human brain, a multiple-demand (MD) network plays a key role in cognitive control, with core components in lateral frontal, dorsomedial frontal and lateral parietal cortex, and multivariate activity patterns that discriminate the contents of many cognitive activities. In prefrontal cortex of the behaving monkey, different cognitive operations are associated with very different patterns of neural activity, while details of a particular stimulus are encoded as small variations on these basic patterns (Sigala etal, 2008). Here, using the advanced fMRI methods of the Human Connectome Project and their 360-region cortical parcellation, we searched for a similar result in MD activation patterns. In each parcel, we compared multivertex patterns for every combination of three tasks (working memory, task-switching, and stop-signal) and two stimulus classes (faces and buildings). Though both task and stimulus category were discriminated in every cortical parcel, the strength of discrimination varied strongly across parcels. The different cognitive operations of the three tasks were strongly discriminated in MD regions. Stimulus categories, in contrast, were most strongly discriminated in a large region of primary and higher visual cortex, and intriguingly, in both parietal and frontal lobe regions adjacent to core MD regions. In the monkey, frontal neurons show a strong pattern of nonlinear mixed selectivity, with activity reflecting specific conjunctions of task events. In our data, however, there was limited evidence for mixed selectivity; throughout the brain, discriminations of task and stimulus combined largely linearly, with a small nonlinear component. In MD regions, human fMRI data recapitulate some but not all aspects of electrophysiological data from nonhuman primates.

A different interpretation of the DIANA fMRI signal.

Direct detection of neural activity by functional magnetic resonance imaging (fMRI) has been a longstanding goal in neuroscience. A recent study argued that it is possible to detect neuroelectrical potentials using a specialized fMRI scanning approach the authors termed "direct imaging of neuronal activity" (DIANA). We implemented DIANA in anesthetized rats and measured responses to somatosensory stimulation, reproducing core findings of the original study. We show, however, that neural activity is neither sufficient nor necessary to produce such results. We use a combination of control conditions and simulations to demonstrate that DIANA signals can arise from nonideal aspects of the pulse sequence and specimen that help determine spatiotemporal characteristics of the data. Our analysis emphasizes a need for cautious interpretation and mechanistic evaluation of advanced fMRI techniques.

Disclosing Results of Tests for Covert Consciousness: A Framework for Ethical Translation.

The advent of neurotechnologies including advanced functional magnetic resonance imaging and electroencephalography to detect states of awareness not detectable by traditional bedside neurobehavioral techniques (i.e., covert consciousness) promises to transform neuroscience research and clinical practice for patients with brain injury. As these interventions progress from research tools into actionable, guideline-endorsed clinical tests, ethical guidance for clinicians on how to responsibly communicate the sensitive results they yield is crucial yet remains underdeveloped. Drawing on insights from empirical and theoretical neuroethics research and our clinical experience with advanced neurotechnologies to detect consciousness in behaviorally unresponsive patients, we critically evaluate ethical promises and perils associated with disclosing the results of clinical covert consciousness assessments and describe a semistructured approach to responsible data sharing to mitigate potential risks.

Cortical areas involved in grasping and reaching actions with and without visual information: An ALE meta-analysis of neuroimaging studies.

The functional specialization of the ventral stream in Perception and the dorsal stream in Action is the cornerstone of the leading model proposed by Goodale and Milner in 1992. This model is based on neuropsychological evidence and has been a matter of debate for almost three decades, during which the dual-visual stream hypothesis has received much attention, including support and criticism. The advent of functional magnetic resonance imaging (fMRI) has allowed investigating the brain areas involved in Perception and Action, and provided useful data on the functional specialization of the two streams. Research on this topic has been quite prolific, yet no meta-analysis so far has explored the spatial convergence in the involvement of the two streams in Action. The present meta-analysis (N=53 fMRI and PET studies) was designed to reveal the specific neural activations associated with Action (i.e., grasping and reaching movements), and the extent to which visual information affects the involvement of the two streams during motor control. Our results provide a comprehensive view of the consistent and spatially convergent neural correlates of Action based on neuroimaging studies conducted over the past two decades. In particular, occipital-temporal areas showed higher activation likelihood in the Vision compared to the No vision condition, but no difference between reach and grasp actions. Frontal-parietal areas were consistently involved in both reach and grasp actions regardless of visual availability. We discuss our results in light of the well-established dual-visual stream model and frame these findings in the context of recent discoveries obtained with advanced fMRI methods, such as multivoxel pattern analysis.

Resting-state functional MRI of the visual system for characterization of optic neuropathy.

Optic neuropathy refers to disease of the optic nerve and can result in loss of visual acuity and/or visual field defects. Combining findings from multiple fMRI modalities can offer valuable information for characterizing and managing optic neuropathies. In this article, we review a subset of resting-state functional magnetic resonance imaging (RS-fMRI) studies of optic neuropathies. We consider glaucoma, acute optic neuritis (ON), discuss traumatic optic neuropathy (TON), and explore consistency between findings from RS and visually driven fMRI studies. Consistent with visually driven studies, glaucoma studies at rest also indicated reduced activation in the visual cortex and dorsal visual stream. RS-fMRI further reported varying levels of functional connectivity in the ventral stream depending on disease severity. ON patients show alterations within the visual cortex in both fMRI techniques. Particularly, higher-than-normal RS activity is observed in the acute phase and decreases as the disease progresses. A similar pattern is observed in the visual cortex of TON-like, open globe injury (OGI), patients. Additionally, visually driven and RS-fMRI studies of ON patients show recovery of brain activity in the visual cortex. RS-fMRI suggests recovery of signals in higher-tier visual areas MT and LOC as well. Finally, RS-fMRI has not yet been applied to TON, although reviewing OGI studies suggests that it is feasible. Future RS-fMRI studies of optic neuropathies could prioritize studying the fine scale RS activity of brain areas that visually driven studies have identified. We suggest that a more systematic longitudinal comparison of optic neuropathies with advanced fMRI would provide improved diagnostic and prognostic information.

The role of ascending arousal network in patients with chronic insomnia disorder.

The ascending arousal system plays a crucial role in individuals' consciousness. Recently, advanced functional magnetic resonance imaging (fMRI) has made it possible to investigate the ascending arousal network (AAN) in vivo. However, the role of AAN in the neuropathology of human insomnia remains unclear. Our study aimed to explore alterations in AAN and its connections with cortical networks in chronic insomnia disorder (CID). Resting-state fMRI data were acquired from 60 patients with CID and 60 good sleeper controls (GSCs). Changes in the brain's functional connectivity (FC) between the AAN and eight cortical networks were detected in patients with CID and GSCs. Multivariate pattern analysis (MVPA) was employed to differentiate CID patients from GSCs and predict clinical symptoms in patients with CID. Finally, these MVPA findings were further verified using an external data set (32 patients with CID and 33 GSCs). Compared to GSCs, patients with CID exhibited increased FC within the AAN, as well as increased FC between the AAN and default mode, cerebellar, sensorimotor, and dorsal attention networks. These AAN-related FC patterns and the MVPA classification model could be used to differentiate CID patients from GSCs with 88% accuracy in the first cohort and 77% accuracy in the validation cohort. Moreover, the MVPA prediction models could separately predict insomnia (data set 1, R2 = .34; data set 2, R2 = .15) and anxiety symptoms (data set 1, R2 = .35; data set 2, R2 = .34) in the two independent cohorts of patients. Our findings indicated that AAN contributed to the neurobiological mechanism of insomnia and highlighted that fMRI-based markers and machine learning techniques might facilitate the evaluation of insomnia and its comorbid mental symptoms.

Visually driven functional MRI techniques for characterization of optic neuropathy

Optic neuropathies are conditions that cause disease to the optic nerve, and can result in loss of visual acuity and/or visual field defects. An improved understanding of how these conditions affect the entire visual system is warranted, to better predict and/or restore the visual loss. In this article, we review visually-driven functional magnetic resonance imaging (fMRI) studies of optic neuropathies, including glaucoma and optic neuritis (ON); we also discuss traumatic optic neuropathy (TON). Optic neuropathy-related vision loss results in fMRI deficit within the visual cortex, and is often strongly correlated with clinical severity measures. Using predominantly flickering checkerboard stimuli, glaucoma studies indicated retinotopic-specific cortical alteration with more prominent deficits in advanced than in early glaucoma. Some glaucoma studies indicate a reorganized visual cortex. ON studies have indicated that the impacted cortical areas are briefly hyperactive. For ON, brain deficits are greater in the acute stages of the disease, followed by (near) normalization of responses of the LGN, visual cortex, and the dorsal visual stream, but not the ventral extrastriate cortex. Visually-driven fMRI is sensitive, at least in ON, in discriminating patients from controls, as well as the affected eye from the fellow eye within patients. The use of a greater variety of stimuli beyond checkerboards (e.g., visual motion and object recognition) in recent ON studies is encouraging, and needs to continue to disentangle the results in terms of change over time. Finally, visually-driven fMRI has not yet been applied in TON, although preliminary efforts suggest it may be feasible. Future fMRI studies of optic neuropathies should consider using more complex visual stimuli, and inter-regional analysis methods including functional connectivity. We suggest that a more systematic longitudinal comparison of optic neuropathies with advanced fMRI would provide improved diagnostic and prognostic information.

BrainIAK: The Brain Imaging Analysis Kit.

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be se amlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

MGluR5 and GABAA receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

The glutamate and γ‐aminobutyric acid neuroreceptor subtypes mGluR5 and GABAA are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non‐displaceable binding potential (BP ND), and total distribution volume (V T) based on simultaneously acquired bolus‐infusion positron emission tomography (PET) and MR data. The pipeline was applied to [11C]ABP688‐PET/MR (13 healthy male non‐smokers, 26.6 ± 7.0 years) and [11C]Flumazenil‐PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as V T and BP ND atlases of mGluR5 and GABAA, were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δABP = 3.0 ± 1.0% and δFMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σABP = 4.0%–16.0%, σFMZ = 3.9%–9.5%). An evaluation of PET‐to‐PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [15O]H2O‐template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions.

Chapter 7 - Resting-state functional connectivity of the human hypothalamus

Communication pathways of the hypothalamus with other brain regions and the periphery are critical to successfully control key physiological and psychological processes. With advanced functional magnetic resonance imaging (fMRI) techniques, it is possible to target hypothalamic function and infer discrete hypothalamus networks. Resting-state functional connectivity (RSFC) is a promising tool to study the functional organization of the brain and may act as a marker of individual differences and dysfunctions. Based on recent fMRI findings, the hypothalamus is mostly connected to parts of the striatum, midbrain, thalamus, insula, frontal, cingulate, and temporal cortices and the cerebellum. There is a strong interplay of the hypothalamus with these regions in response to different metabolic, hormonal, and nutritional states. In a state of hunger, hypothalamus RSFC increases with a strong shift to reward-related brain regions, especially in person with excessive weight. Nutrient signals and hormones, as insulin, act on these same connections conveying reward and internal signals to regulate homeostatic control. Moreover, dysfunctional hypothalamus communication has been documented in persons with neurological and psychiatric diseases. The results implicate that patients with depression, epilepsy, and neurodegenerative diseases show mostly a reduction in hypothalamus RSFC, whereas patients with migraine and headache display predominantly increased hypothalamus RSFC. The extent of these changes and regions affected depend on the disorder and symptom severity. Whether hypothalamus RSFC can serve as a marker for disease states or is a prodromal neurobiological feature still needs to be investigated.

New acquisition techniques and their prospects for the achievable resolution of fMRI.

This work reviews recent advances in technologies for functional magnetic resonance imaging (fMRI) of the human brain and highlights the push for higher functional specificity based on increased spatial resolution and specific MR contrasts to reveal previously undetectable functional properties of small-scale cortical structures. We discuss how the combination of MR hardware, advanced acquisition techniques and various MR contrast mechanisms have enabled recent progress in functional neuroimaging. However, these advanced fMRI practices have only been applied to a handful of neuroscience questions to date, with the majority of the neuroscience community still using conventional imaging techniques. We thus discuss upcoming challenges and possibilities for fMRI technology development in human neuroscience. We hope that readers interested in functional brain imaging acquire an understanding of current and novel developments and potential future applications, even if they don't have a background in MR physics or engineering. We summarize the capabilities of standard fMRI acquisition schemes with pointers to relevant literature and comprehensive reviews and introduce more recent developments.

Evaluating the Treatment Efficacy of Nano-Drug in a Lung Cancer Model Using Advanced Functional Magnetic Resonance Imaging.

ObjectivesNano-drug delivery system is an interesting field in precise cancer treatment, but few study has reported the microenvironmental changes after such treatment. This study aimed to detect the hemodynamic and microenvironmental changes in a lung cancer xenograft model after treated with doxorubicin (DOX) encapsulated by a cyclic arginine-glycine-aspartic acid polypeptide modified poly-(lactic-co-glycolic acid) nanosystem (cRGD-PLGA@DOX) using functional magnetic resonance imaging.Materials and MethodsThirty-two tumor-bearing mice were randomly divided into four groups. Group A was treated with 0.9% saline, Group B with 4 mg/kg of doxorubicin, Group C with 2 mg/kg of cRGD-PLGA@DOX, and Group D with 4 mg/kg of cRGD-PLGA@DOX. Intravoxel incoherent motion diffusion-weighed imaging (IVIM-DWI) and R2∗ mapping were performed, and D∗, f, D, and R2∗ values were obtained before and1, 2, and 3 weeks after treatment. They were sacrificed for pathological examination after examinations.ResultsThe reconstructed cRGD-PLGA@DOX was homogeneous, well-dispersed, and spherical in shape, with an average size of 180 nm. Group D demonstrated the smallest tumor volume and highest tumor inhibition rate in 3 weeks. D value of Group B, C, and D manifested an upward trend in 3 weeks with the highest increase in Group D. D∗ values shared a similar increased trends with f values in Group A, B, and C in 3 weeks, except Group D. R2∗ value of Group A gradually increased in 3 weeks, but the trends were reversed in the treatment groups. D value was significantly negative with Ki-67 expression (r = −0.757, P < 0.001) but positive with TUNEL (r = 0.621, P < 0.001), and phosphate and tension homology deleted on chromosome ten (PTEN) staining (r = 0.57, P = 0.004). R2∗ value was closely correlated with HIF-1a (r = 0.721, P < 0.001).ConclusionThe nano-drug demonstrated an enhanced anti-tumor effect without the need of increased chemotherapeutic dosage. The tumor microenvironment such as cellular and perfusion changes during treatment can be non-invasively detected by two functional MRI including IVIM-DWI and R2∗ mapping.

BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis.

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

Changes in neurocognitive function and central nervous system structure in childhood acute lymphoblastic leukaemia survivors after treatment: a meta-analysis.

Acute lymphoblastic leukaemia (ALL) is the most common malignancy in children. Although the survival rate has increased dramatically over the last decades, patients struggle with the adverse side effects of treatment. Treatment for ALL includes chemotherapy and irradiation - both of which are linked to cognitive impairments and alterations in central nervous system (CNS) structure and function detected by neuroimaging and in neurocognitive studies. The present article is a meta-analysis of the existing evidence for the mechanisms underlying changes in the CNS and neurocognitive function in ALL survivors after treatment. We found that compared with controls, ALL survivors develop: (i) cognitive sequelae in intelligence, academics, attention, memory, processing speed and executive function domains; (ii) decreased grey and white matter volume in cortical and several subcortical brain regions, with functional changes particularly in frontal regions and the hippocampus; (iii) neurocognitive impairments related to CNS changes; and (iv) reduction, but not resolution, of late neurocognitive sequelae in patients in whom prophylactic irradiation was replaced by systemic/intrathecal chemotherapy. Continued work with advanced functional magnetic resonance imaging techniques will hopefully allow the detection of early CNS changes as biomarkers to help guide early diagnosis and intervention for neurocognitive defects in patients with childhood ALL.

Award for Distinguished Scientific Early Career Contributions to Psychology: Julie D. Golomb.

The APA Awards for Distinguished Scientific Early Career Contributions to Psychology recognize psychologists who have demonstrated excellence early in their careers and have held a doctoral degree for no more than 9 years. One of the 2019 award winners is Julie D. Golomb, "for outstanding research on the intricate relationship between eye movements, visual perception, attention, and memory. Julie D. Golomb's work relies on creative behavioral experimentation, advanced functional magnetic resonance imaging (fMRI) methods, and rigorous analysis to reveal the importance of 'eye-centered' retinotopic representations at many levels of the human visual system. Her work has expanded our understanding of how visual information is updated and integrated across successive eye movements, thereby allowing us to achieve a conscious impression of a stable visual world." (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Pharmaco-fMRI: A Tool to Predict the Response to Antiepileptic Drugs in Epilepsy.

Pharmacological treatment with antiepileptic medications (AEDs) in epilepsy is associated with a variety of neurocognitive side effects. However, the mechanisms underlying these side effects, and why certain brain anatomies are more affected still remain poorly understood. Advanced functional magnetic resonance imaging (fMRI) methods, such as pharmaco-fMRI, can investigate medication-related effects on brain activities using task and resting state fMRI and showing reproducible activation and deactivation patterns. This methodological approach has been used successfully to complement neuropsychological studies of AEDs. Here we review pharmaco-fMRI studies in people with epilepsy targeting the most-widely prescribed AEDs. Pharmco-fMRI has advanced our understanding of the impact of AEDs on specific brain networks and thus may provide potential biomarkers to move beyond the current “trial and error” approach when commencing anti-epileptic medication.

High-resolution magnetic resonance imaging at 3T of pituitary gland: advantages and pitfalls.

Magnetic resonance imaging (MRI) is the primary imaging tool for the evaluation of pituitary gland pathology. In the last decades, MRI with high-field scanners has become widely used in clinical practice, leading to significant improvements in image quality mainly thanks to a superior spatial resolution and signal intensity. Moreover, several advanced functional MRI sequences have been implemented for pituitary imaging, providing valuable information in diagnostic and presurgical planning of pituitary adenomas. Higher field strength presents however some technical pitfalls to be aware of. The purpose of this article is to review the state of the art of high-resolution MRI of the pituitary gland at 3 Tesla (3T), with a particular focus on the main benefits and the possible limitations of higher field imaging.

Long-term efficacy of Chinese medicine Bushen Capsule on cognition and brain activity in patients with amnestic mild cognitive impairment

Mild cognitive impairment (MCI), regarded as the prodromal stage before the clinical phase of Alzheimer’s disease (AD), has been considered for early intervention. Unfortunately, many trials in this stage with drugs with single-target turned out to be little or no effect. Multi-targeting in nature based on the theory of Traditional Chinese Medicine (TCM) offers another prospect for intervention. Together with advanced functional magnetic resonance imaging (fMRI) technique for more sensitive and objective evaluation, we investigated the long-term therapeutic effects of a TCM compound on cognition and task-related neuronal activity. Sixty amnestic MCI (aMCI) participants from randomly divided into drug (30 with Bushen capsules (BSC)) and placebo (30 with placebo capsules) groups for this 2-years trial. Neuropsychological and N-back task-fMRI data were acquired at baseline and two follow-ups to assess, via linear mixed effect models, the changes of cognitive ability and brain activation over treatments. The drug group, compared with placebo group, exhibited improvement or stabilization in memory measures over time. Analyses of fMRI revealed that the placebo group exhibited higher activation magnitude and spatial extents at left superior parietal lobule; importantly, the greater activation identified in placebo group was related to more decline in the digit span. BSC showed long-term ameliorative effects on cognitive performances in aMCI patients, which might result from the regulation of abnormal brain activities. Our study provided evidence for the potential of TCM in early prevention of AD, as well as the feasibility of neuroimaging biomarkers in clinical trials.

Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback

Real-time functional magnetic resonance imaging (fMRI) neurofeedback is an experimental framework in which fMRI signals are presented to participants in a real-time manner to change their behaviors. Changes in behaviors after real-time fMRI neurofeedback are postulated to be caused by neural plasticity driven by the induction of specific targeted activities at the neuronal level (targeted neural plasticity model). However, some research groups argued that behavioral changes in conventional real-time fMRI neurofeedback studies are explained by alternative accounts, including the placebo effect and physiological artifacts. Recently, decoded neurofeedback (DecNef) has been developed as a result of adapting new technological advancements, including implicit neurofeedback and fMRI multivariate analyses. DecNef provides strong evidence for the targeted neural plasticity model while refuting the abovementioned alternative accounts. In this review, we first discuss how DecNef refutes the alternative accounts. Second, we propose a model that shows how targeted neural plasticity occurs at the neuronal level during DecNef training. Finally, we discuss computational and empirical evidence that supports the model. Clarification of the neural mechanisms of DecNef would lead to the development of more advanced fMRI neurofeedback methods that may serve as powerful tools for both basic and clinical research.