Real-time Functional MRI Research Articles

Validation of real-time fMRI neurofeedback procedure for cognitive training using counterbalanced active-sham study design

Investigation of neural mechanisms of real-time functional MRI neurofeedback (rtfMRI-nf) training requires an efficient study control approach. A common rtfMRI-nf study design involves an experimental group, receiving active rtfMRI-nf, and a control group, provided with sham rtfMRI-nf. We report the first study in which rtfMRI-nf procedure is controlled through counterbalancing training runs with active and sham rtfMRI-nf for each participant. Healthy volunteers (n = 18) used rtfMRI-nf to upregulate fMRI activity of an individually defined target region in the left dorsolateral prefrontal cortex (DLPFC) while performing tasks that involved mental generation of a random numerical sequence and serial summation of numbers in the sequence. Sham rtfMRI-nf was provided based on fMRI activity of a different brain region, not involved in these tasks. The experimental procedure included two training runs with the active rtfMRI-nf and two runs with the sham rtfMRI-nf, in a randomized order. The participants achieved significantly higher fMRI activation of the left DLPFC target region during the active rtfMRI-nf conditions compared to the sham rtfMRI-nf conditions. fMRI functional connectivity of the left DLPFC target region with the nodes of the central executive network was significantly enhanced during the active rtfMRI-nf conditions relative to the sham conditions. fMRI connectivity of the target region with the nodes of the default mode network was similarly enhanced. fMRI connectivity changes between the active and sham conditions exhibited meaningful associations with individual performance measures on the Working Memory Multimodal Attention Task, the Approach-Avoidance Task, and the Trail Making Test. Our results demonstrate that the counterbalanced active-sham study design can be efficiently used to investigate mechanisms of active rtfMRI-nf in direct comparison to those of sham rtfMRI-nf. Further studies with larger group sizes are needed to confirm the reported findings and evaluate clinical utility of this study control approach.

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.

Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Altered amygdala functional connectivity after real-time functional MRI emotion self-regulation training.

Real-time functional MRI neurofeedback (rtfMRI-NF) is a noninvasive technique that extracts concurrent brain states and provides feedback to subjects in an online method. Our study aims to investigate the effect of rtfMRI-NF on amygdala-based emotion self-regulation by analyzing resting-state functional connectivity. We conducted a task experiment to train subjects in self-regulating amygdala activity in response to emotional stimuli. Twenty subjects were divided into two groups. The up-regulate group (URG) viewed positive stimulus, while the down-regulate group (DRG) viewed negative stimulus. The rtfMRI-NF experiment paradigm consisted of three conditions. The URG's percent amplitude fluctuation (PerAF) scores are significant, indicating that positive emotions may be a partial side effect, with increased activity in the left hemisphere. Resting-state functional connectivity was analyzed via a paired-sample t-test before and after neurofeedback training. Brain network properties and functional connectivity analysis showed a significant difference between the default mode network (DMN) and the brain region associated with the limbic system. These results reveal to some extent the mechanism of neurofeedback training to improve individuals' emotional regulate regulation ability. Our study has shown that rtfMRI-neurofeedback training can effectively enhance the ability to voluntarily control brain responses. Furthermore, the results of the functional analysis have revealed distinct changes in the amygdala functional connectivity circuits following rtfMRI-neurofeedback training. These findings may suggest the potential clinical applications of rtfMRI-neurofeedback as a new therapy for emotionally related mental disorders.

Self-regulation of the posterior cingulate cortex with real-time fMRI neurofeedback augmented mindfulness training in healthy adolescents: A nonrandomized feasibility study

Mindfulness training (MT) promotes the development of one’s ability to observe and attend to internal and external experiences with objectivity and nonjudgment with evidence to improve psychological well-being. Real-time functional MRI neurofeedback (rtfMRI-nf) is a noninvasive method of modulating activity of a brain region or circuit. The posterior cingulate cortex (PCC) has been hypothesized to be an important hub instantiating a mindful state. This nonrandomized, single-arm study examined the feasibility and tolerability of training typically developing adolescents to self-regulate the posterior cingulate cortex (PCC) using rtfMRI-nf during MT. Thirty-four adolescents (mean age: 15 years; 14 females) completed the neurofeedback augmented mindfulness training task, including Focus-on-Breath (MT), Describe (self-referential thinking), and Rest conditions, across three neurofeedback and two non-neurofeedback runs (Observe, Transfer). Self-report assessments demonstrated the feasibility and tolerability of the task. Neurofeedback runs differed significantly from non-neurofeedback runs for the Focus-on-Breath versus Describe contrast, characterized by decreased activity in the PCC during the Focus-on-Breath condition (z = −2.38 to −6.27). MT neurofeedback neural representation further involved the medial prefrontal cortex, anterior cingulate cortex, dorsolateral prefrontal cortex, posterior insula, hippocampus, and amygdala. State awareness of physical sensations increased following rtfMRI-nf and was maintained at 1-week follow-up (Cohens’ d = 0.69). Findings demonstrate feasibility and tolerability of rtfMRI-nf in healthy adolescents, replicates the role of PCC in MT, and demonstrate a potential neuromodulatory mechanism to leverage and streamline the learning of mindfulness practice. (ClinicalTrials.gov identifier #NCT04053582; August 12, 2019).

Real-Time Functional MRI in the Treatment of Mental Health Disorders.

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Correlations Between the DMN and the Smoking Cessation Outcome of a Real-Time fMRI Neurofeedback Supported Exploratory Therapy Approach: Descriptive Statistics on Tobacco-Dependent Patients.

The aim of this study was to explore the potential of default mode network (DMN) functional connectivity for predicting the success of smoking cessation in patients with tobacco dependence in the context of a real-time function al MRI (RT-fMRI) neurofeedback (NF) supported therapy.Fifty-four tobacco-dependent patients underwent three RT-fMRI-NF sessions including resting-state functional connectivity (RSFC) runs over a period of 4 weeks during professionally assisted smoking cessation. Patients were randomized into two groups that performed either active NF of an addiction-related brain region or sham NF. After preprocessing, the RSFC baseline data were statistically evaluated using seed-based ROI (SBA) approaches taking into account the smoking status of patients after 3 months (abstinence/relapse).The results of the real study group showed a widespread functional connectivity in the relapse subgroup (n = 10) exceeding the DMN template and mainly low correlations and anticorrelations in the within-seed analysis. In contrast, the connectivity pattern of the abstinence subgroup (n = 8) primarily contained the core DMN in the seed-to-whole-brain analysis and a left lateralized correlation pattern in the within-seed analysis. Calculated Multi-Subject Dictionary Learning (MSDL) matrices showed anticorrelations between DMN regions and salience regions in the abstinence group. Concerning the sham group, results of the relapse subgroup (n = 4) and the abstinence subgroup (n = 6) showed similar trends only in the within-seed analysis.In the setting of a RT-fMRI-NF-assisted therapy, a widespread intrinsic DMN connectivity and a low negative coupling between the DMN and the salience network (SN) in patients with tobacco dependency during early withdrawal may be useful as an early indicator of later therapy nonresponse.

Functional and clinical outcomes of FMRI-based neurofeedback training in patients with alcohol dependence: a pilot study

Identifying treatment options for patients with alcohol dependence is challenging. This study investigates the application of real-time functional MRI (rtfMRI) neurofeedback (NF) to foster resistance towards craving-related neural activation in alcohol dependence. We report a double-blind, placebo-controlled rtfMRI study with three NF sessions using alcohol-associated cues as an add-on therapy to the standard treatment. Fifty-two patients (45 male; 7 female) diagnosed with alcohol dependence were recruited in Munich, Germany. RtfMRI data were acquired in three sessions and clinical abstinence was evaluated 3 months after the last NF session. Before the NF training, BOLD responses and clinical data did not differ between groups, apart from anger and impulsiveness. During NF training, BOLD responses of the active group were decreased in medial frontal areas/caudate nucleus, and increased, e.g. in the cuneus/precuneus and occipital cortex. Within the active group, the down-regulation of neuronal responses was more pronounced in patients who remained abstinent for at least 3 months after the intervention compared to patients with a relapse. As BOLD responses were comparable between groups before the NF training, functional variations during NF cannot be attributed to preexisting distinctions. We could not demonstrate that rtfMRI as an add-on treatment in patients with alcohol dependence leads to clinically superior abstinence for the active NF group after 3 months. However, the study provides evidence for a targeted modulation of addiction-associated brain responses in alcohol dependence using rtfMRI.

Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: empirical evidence from functional MRI data.

Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training. Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs). Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs. Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training.

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional ‘one-size-fits-all’ approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.

Hippocampal volume recovery with real-time functional MRI amygdala neurofeedback emotional training for posttraumatic stress disorder

BackgroundSmall hippocampal volume is a prevalent neurostructural abnormality in posttraumatic stress disorder (PTSD). However, whether the hippocampal atrophy is the cause of disease symptoms or a pre-existing risk factor and whether it is a reversible alteration or a permanent trait are unclear. The trait- or state-dependent alteration could also differ among the hippocampal subfields. MethodsThe study examined the longitudinal hippocampal volume changes due to positive emotional training with left amygdala (LA) real-time fMRI neurofeedback (rtfMRI-nf) in combat veterans with PTSD. The participants were trained to increase the neurofeedback signal from LA (experimental group, N = 20) or brain region not involved in emotion processing (control group, N = 9) by recalling a positive autobiographical memory. The pre- and post-training structural MRI brain images were processed with FreeSurfer to evaluate the hippocampal subfield volumes. Hippocampal volumes for healthy controls (N = 43) were also examined to evaluate the baseline abnormality in PTSD. ResultsA significant group difference in volume change was found in the left CA1 head region. This region had the most significant volume reduction at the baseline in PTSD. The experimental group showed a significant volume increase, while the control group showed a significant volume decrease in this region. The volume change in the control group negatively correlated with interval days between the scans. LimitationsA cognitive improvement due to the hippocampal volume increase could not be found with symptom scales. ConclusionsRtfMRI-nf positive emotional training increased the hippocampus volume among people with PTSD, suggesting that hippocampal atrophy in PTSD is modifiable.

Virtual reality and real-time neurofeedback functional MRI: a breakthrough in foreseeing Alzheimer's disease?

This scientific commentary refers to ‘Earliest amyloid and tau deposition modulate the influence of limbic networks during closed-loop hippocampal downregulation’ by Skouras etal. (doi:10.1093/brain/awaa011).

Earliest amyloid and tau deposition modulate the influence of limbic networks during closed-loop hippocampal downregulation

Research into hippocampal self-regulation abilities may help determine the clinical significance of hippocampal hyperactivity throughout the pathophysiological continuum of Alzheimer's disease. In this study, we aimed to identify the effects of amyloid-β peptide 42 (amyloid-β42) and phosphorylated tau on the patterns of functional connectomics involved in hippocampal downregulation. We identified 48 cognitively unimpaired participants (22 with elevated CSF amyloid-β peptide 42 levels, 15 with elevated CSF phosphorylated tau levels, mean age of 62.705 ± 4.628 years), from the population-based 'Alzheimer's and Families' study, with baseline MRI, CSF biomarkers, APOE genotyping and neuropsychological evaluation. We developed a closed-loop, real-time functional MRI neurofeedback task with virtual reality and tailored it for training downregulation of hippocampal subfield cornu ammonis 1 (CA1). Neurofeedback performance score, cognitive reserve score, hippocampal volume, number of apolipoprotein ε4 alleles and sex were controlled for as confounds in all cross-sectional analyses. First, using voxel-wise multiple regression analysis and controlling for CSF biomarkers, we identified the effect of healthy ageing on eigenvector centrality, a measure of each voxel's overall influence based on iterative whole-brain connectomics, during hippocampal CA1 downregulation. Then, controlling for age, we identified the effects of abnormal CSF amyloid-β42 and phosphorylated tau levels on eigenvector centrality during hippocampal CA1 downregulation. Across subjects, our main findings during hippocampal downregulation were: (i) in the absence of abnormal biomarkers, age correlated with eigenvector centrality negatively in the insula and midcingulate cortex, and positively in the inferior temporal gyrus; (ii) abnormal CSF amyloid-β42 (<1098) correlated negatively with eigenvector centrality in the anterior cingulate cortex and primary motor cortex; and (iii) abnormal CSF phosphorylated tau levels (>19.2) correlated with eigenvector centrality positively in the ventral striatum, anterior cingulate and somatosensory cortex, and negatively in the precuneus and orbitofrontal cortex. During resting state functional MRI, similar eigenvector centrality patterns in the cingulate had previously been associated to CSF biomarkers in mild cognitive impairment and dementia patients. Using the developed closed-loop paradigm, we observed such patterns, which are characteristic of advanced disease stages, during a much earlier presymptomatic phase. In the absence of CSF biomarkers, our non-invasive, interactive, adaptive and gamified neuroimaging procedure may provide important information for clinical prognosis and monitoring of therapeutic efficacy. We have released the developed paradigm and analysis pipeline as open-source software to facilitate replication studies.

Activity or connectivity? A randomized controlled feasibility study evaluating neurofeedback training in Huntington's disease.

Non-invasive methods, such as neurofeedback training, could support cognitive symptom management in Huntington’s disease by targeting brain regions whose function is impaired. The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of neurofeedback training in Huntington’s disease by examining two different methods, activity and connectivity real-time functional MRI neurofeedback training. Thirty-two Huntington’s disease gene-carriers completed 16 runs of neurofeedback training, using an optimized real-time functional MRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the supplementary motor area, and another receiving neurofeedback based on the correlation of supplementary motor area and left striatum activity (connectivity neurofeedback training), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during neurofeedback training sessions and after training at follow-up sessions. Transfer of training was measured by measuring the participants’ ability to upregulate neurofeedback training target levels without feedback (near transfer), as well as by examining change in objective, a priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher neurofeedback training target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two neurofeedback training methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and learning success. We conclude that although there is evidence that neurofeedback training can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust.

Targeting Hippocampal Hyperactivity With Real-time Functional MRI Based Neurofeedback in Elderly Individuals With and Without Memory Problems

Targeting Hippocampal Hyperactivity With Real-time Functional MRI Based Neurofeedback in Elderly Individuals With and Without Memory Problems

Self-regulation of language areas using real-time functional MRI in stroke patients with expressive aphasia.

The objectives of this study were to test (i) If stroke patients with expressive Aphasia could learn to up-regulate the Blood Oxygenation Level Dependent (BOLD) signal in language areas of the brain, namely Inferior Frontal Gyrus (Broca's area) and Superior Temporal Gyrus (Wernicke's area), with real-time fMRI based neurofeedback of the BOLD activation and functional connectivity between the language areas; and (ii) acquired up-regulation could lead to an improvement in expression of language. The study was performed on three groups: Group 1 (n = 4) of Test patients and group 2 (n = 4) of healthy volunteers underwent the neurofeedback training, whereas group 3 (n = 4) of Control patients underwent treatment as usual. Language performance and recovery were assessed using western aphasia battery and picture naming tasks, before and after the neurofeedback training. Results show that the Test group had significant increase in activation of the Broca's area and its right homologue, while the Normal group achieved the greatest activation during neurofeedback. For the Test group both perilesional and contralateral activations were observed. The improvement in language ability of the test patients was not significantly greater than that of the control patients. Neurofeedback training in Aphasia patients induced significant activation of the Broca's area, Wernicke's area and their right homologues, although healthy individuals achieved greater activations in these regions than the patient groups. Training also activated perilesional areas of Rolandic operculum, precentral gyrus and postcentral gyrus for the Test patients significantly. However, lack of behavioral and symptom modifications in the Test group calls for improvements in the efficacy of the approach.

Data-driven tensor independent component analysis for model-based connectivity neurofeedback

Neurofeedback based on real-time functional MRI is an emerging technique to train voluntary control over brain activity in healthy and disease states. Recent developments even allow for training of brain networks using connectivity feedback based on dynamic causal modeling (DCM). DCM is an influential hypothesis-driven approach that requires prior knowledge about the target brain network dynamics and the modulatory influences. Data-driven approaches, such as tensor independent component analysis (ICA), can reveal spatiotemporal patterns of brain activity without prior assumptions. Tensor ICA allows flexible data decomposition and extraction of components consisting of spatial maps, time-series, and session/subject-specific weights, which can be used to characterize individual neurofeedback regulation per regulation trial, run, or session. In this study, we aimed to better understand the spatiotemporal brain patterns involved and affected by model-based feedback regulation using data-driven tensor ICA. We found that task-specific spatiotemporal brain patterns obtained using tensor ICA were highly consistent with model-based feedback estimates. However, we found that the DCM approach captured specific network interdependencies that went beyond what could be detected with either general linear model (GLM) or ICA approaches. We also found that neurofeedback-guided regulation resulted in activity changes that were characteristic of the mental strategies used to control the feedback signal, and that these activity changes were not limited to periods of active self-regulation, but were also evident in distinct gradual recovery processes during subsequent rest periods. Complementary data-driven and model-based approaches could aid in interpretation of the neurofeedback data when applied post-hoc, and in the definition of the target brain area/pattern/network/model prior to the neurofeedback training study when applied to the pilot data. Systematically investigating the triad of mental effort, spatiotemporal brain network changes, and activity and recovery processes might lead to a better understanding of how learning with neurofeedback is accomplished, and how such learning can cause plastic brain changes along with specific behavioral effects.

Cognitive Improvement and Brain Changes after Real-Time Functional MRI Neurofeedback Training in Healthy Elderly and Prodromal Alzheimer's Disease.

Cognitive decline is characteristic for Alzheimer's disease (AD) and also for healthy ageing. As a proof-of-concept study, we examined whether this decline can be counteracted using real-time fMRI neurofeedback training. Visuospatial memory and the parahippocampal gyrus (PHG) were targeted. Sixteen healthy elderly subjects (mean age 63.5 years, SD = 6.663) and 10 patients with prodromal AD (mean age 66.2 years, SD = 8.930) completed the experiment. Four additional healthy subjects formed a sham-feedback condition to validate the paradigm. The protocol spanned five examination days (T1-T5). T1 contained a neuropsychological pre-test, the encoding of a real-world footpath, and an anatomical MRI scan of the brain. T2-T4 included the fMRI neurofeedback training paradigm, in which subjects learned to enhance activation of the left PHG while recalling the path encoded on T1. At T5, the neuropsychological post-test and another anatomical MRI brain scan were performed. The neuropsychological battery included the Montreal Cognitive Assessment (MoCA); the Visual and Verbal Memory Test (VVM); subtests of the Wechsler Memory Scale (WMS); the Visual Patterns Test; and Trail Making Tests (TMT) A and B. Healthy elderly and patients with prodromal AD showed improved visuospatial memory performance after neurofeedback training. Healthy subjects also performed better in a working-memory task (WMS backward digit-span) and in the MoCA. Both groups were able to elicit parahippocampal activation during training, but no significant changes in brain activation were found over the course of the training. However, Granger-causality-analysis revealed changes in cerebral connectivity over the course of the training, involving the parahippocampus and identifying the precuneus as main driver of activation in both groups. Voxel-based morphometry showed increases in grey matter volumes in the precuneus and frontal cortex. Neither cognitive enhancements, nor parahippocampal activation were found in the control group undergoing sham-feedback. These findings suggest that cognitive decline, either related to prodromal AD or healthy ageing, could be counteracted using fMRI-based neurofeedback. Future research needs to determine the potential of this method as a treatment tool.

Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall

Patients with depression show blunted amygdala hemodynamic activity to positive stimuli, including autobiographical memories. The authors examined the therapeutic efficacy of real-time functional MRI neurofeedback (rtfMRI-nf) training aimed at increasing the amygdala's hemodynamic response to positive memories in patients with depression. In a double-blind, placebo-controlled, randomized clinical trial, unmedicated adults with depression (N=36) were randomly assigned to receive two sessions of rtfMRI-nf either from the amygdala (N=19) or from a parietal control region not involved in emotional processing (N=17). Clinical scores and autobiographical memory performance were assessed at baseline and 1 week after the final rtfMRI-nf session. The primary outcome measure was change in score on the Montgomery-Åsberg Depression Rating Scale (MADRS), and the main analytic approach consisted of a linear mixed-model analysis. In participants in the experimental group, the hemodynamic response in the amygdala increased relative to their own baseline and to the control group. Twelve participants in the amygdala rtfMRI-nf group, compared with only two in the control group, had a >50% decrease in MADRS score. Six participants in the experimental group, compared with one in the control group, met conventional criteria for remission at study end, resulting in a number needed to treat of 4. In participants receiving amygdala rtfMRI-nf, the percent of positive specific memories recalled increased relative to baseline and to the control group. rtfMRI-nf training to increase the amygdala hemodynamic response to positive memories significantly decreased depressive symptoms and increased the percent of specific memories recalled on an autobiographical memory test. These data support a role of the amygdala in recovery from depression.

Self-regulating positive emotion networks by feedback of multiple emotional brain states using real-time fMRI.

Disordered emotion regulation may affect work efficiency, induce social disharmony, and even cause psychiatric diseases. Despite recent neurocomputing advances, whether positive and negative emotion networks can be voluntarily modulated is still unknown. In the present study, we addressed this question through multivariate voxel pattern analysis and real-time functional MRI neurofeedback (rtfMRI-nf). During a sustained emotion regulation task, participants' emotional states (positive or negative) were given to them as feedback. Participants were able to increase the percentage of positive emotional states, enhancing emotion regulation network activities. Participants showed an improvement on the positive subscale of positive and negative affect scale that came close to significance. Furthermore, the activation of several emotion-related brain regions, including insula, amygdala, anterior cingulate cortex, and dorsomedial prefrontal cortex, was also increased during rtfMRI-nf training. These findings suggest that humans are able to voluntarily modulate positive emotion networks, leading to exciting applications in the treatment of various neurological and psychiatric disorders.

EP 131. Real-time fMRI neurofeedback training in elderly leads to cognitive improvement and changes in cerebral connectivity

Introduction During past years real-time functional MRI (rtfMRI) has been shown to be a feasible tool for neurofeedback (NFB). This is a technique, which aims at subjects learning to voluntarily modulate activation of specific brain regions. It has already been applied successfully clinically and non-clinically. We conducted rtfMRI NFB training of the left parahippocampal gyrus (PHG) in healthy elderly with the hypothesis subjects are indeed able to modulate left PHG activation and that this training leads to increased cognitive performance. Here, we also focus on brain connectivity over the course of the training. Methods 16 subjects (mean age 63.5 y, 9 male) completed the protocol of five examination days (T1–T5). On T1 the neuropsychological pre-test was done (see table), followed by the encoding of a real-world footpath (one out of three variants). On T2–T4 the rtfMRI NFB training was carried out. Activation of the left PHG was fed back visually by a thermometer bar. The task alternated between upregulation (recalling the path encoded on T1) and baseline (counting backwards). Three rtfMRI NFB runs were done per day. On T5 the neuropsychological post-test was done. For analysis of neuropsychological data ANCOVAs with examination day and variant of the footpath as factors and age, education and gender as covariates were calculated. For fMRI data a general linear model with upregulation as predictor was computed with BrainVoyager QX for each NFB run and the activation was then averaged across all runs. Activation was limited to a threshold of Bonferroni-corrected p . 05 and two voxels of cluster size. To conduct an analysis of connectivity, activation time courses were extracted from clusters active during upregulation and averaged for each day and region. With the statistical software R, a Granger-Causality-Analysis (GCA) for each training day was conducted for each combination of active regions (306 connections) with a lag size of 1 volume (2000 ms). P-values were corrected using the Bonferroni-Holm method. Results Subjects showed improvements in the following neuropsychological tests: a visuo-spatial-memory task from the VVM (F(1,13) = 8.378, p = .013), the backward-digit-span task from the WMS (F(1,13) = 6.919, p = .021) and the MoCA (F(1,13) = 12.204, p = .004). Functional MRI analysis revealed that during NFB training the left PHG was indeed activated. Activation was further found in the right PHG, the left posterior cingulate, bilaterally in the precuneus, superior occipital lobe, middle frontal gyrus and the cerebellum. GCA revealed 133 connections on T2, 206 on T3 and 156 on T4. The main findings were: (1) activation was chiefly driven by both anterior lobes of the cerebellum, the left posterior cingulate and a cluster within the right precuneus, but these regions received only few input from other regions; (2) there was no connectivity between both PHG; (3) the amount of connections to both PHG increased over time, while the amount of connections from both PHG to other regions such as the right cerebellum and the right precuneus tended to decrease. Conclusions The neuropsychological, activation and connectivity data from our NFB training in elderly suggest the following: (1) subjects were able to modulate left PHG activation; (2) the conducted training lead to increased cognitive performance; (3) GCA revealed a complex underlying neuronal network, mainly driven by a small number of regions and with a changing role of the target region of the training over time.