Fire Kasina advanced meditation produces experiences comparable to psychedelic and near-death experiences: A pilot study.

Psychedelic-assisted therapy studies suggest that the induction of "mystical experiences" combined with psycho-therapy is a possible intervention for psychiatric illness. Advanced meditation may induce powerful experiences comparable to psychedelics. We investigated effects of an intensive meditation practice called Fire Kasina. Six individuals completed a retreat, and participated in an interview in which they described their experiences. They also completed the Revised Mystical Experience Questionnaire (MEQ), Hood Mystical Experience Scale (HME), and Cole's Spiritual Transformation Scale. Mean MEQ scores were 85 %, similar to prior observations of high-dose psilocybin and were stronger than moderate-dose psilocybin (t(5) = 4.41, p = 0.007, d = 1.80; W(5) = 21, p = 0.031). Mean HME scores were 93 %, exceeding levels reported for NDEs (mean 74 %) and high-dose psilocybin (mean 77 %). In qualitative analysis, experiences were described as the most intense of the individual's life, while subsequent transformational effects included substantial shifts in worldview.

Neuromodulation and meditation: A review and synthesis toward promoting well-being and understanding consciousness and brain.

The neuroscience of meditation is providing insight into meditation's beneficial effects on well-being and informing understanding of consciousness. However, further research is needed to explicate mechanisms linking brain activity and meditation. Non-invasive brain stimulation (NIBS) presents a promising approach for causally investigating neural mechanisms of meditation. Prior NIBS-meditation research has predominantly targeted frontal and parietal cortices suggesting that it might be possible to boost the behavioral and neural effects of meditation with NIBS. Moreover, NIBS has revealed distinct neural signatures in long-term meditators. Nonetheless, methodological variations in NIBS-meditation research contributes to challenges for definitive interpretation of previous results. Future NIBS studies should further investigate core substrates of meditation, including specific brain networks and oscillations, and causal neural mechanisms of advanced meditation. Overall, NIBS-meditation research holds promise for enhancing meditation-based interventions in support of well-being and resilience in both non-clinical and clinical populations, and for uncovering the brain-mind mechanisms of meditation and consciousness.

The Mindful Brain: A Systematic Review of the Neural Correlates of Trait Mindfulness.

Trait self-report mindfulness scales measure one's disposition to pay nonjudgmental attention to the present moment. Concerns have been raised about the validity of trait mindfulness scales. Despite this, there is extensive literature correlating mindfulness scales with objective brain measures, with the goal of providing insight into mechanisms of mindfulness, and insight into associated positive mental health outcomes. Here, we systematically examined the neural correlates of trait mindfulness. We assessed 68 correlational studies across structural magnetic resonance imaging, task-based fMRI, resting-state fMRI, and EEG. Several consistent findings were identified, associating greater trait mindfulness with decreased amygdala reactivity to emotional stimuli, increased cortical thickness in frontal regions and insular cortex regions, and decreased connectivity within the default-mode network. These findings converged with results from intervention studies and those that included mindfulness experts. On the other hand, the connections between trait mindfulness and EEG metrics remain inconclusive, as do the associations between trait mindfulness and between-network resting-state fMRI metrics. ERP measures from EEG used to measure attentional or emotional processing may not show reliable individual variation. Research on body awareness and self-relevant processing is scarce. For a more robust correlational neuroscience of trait mindfulness, we recommend larger sample sizes, data-driven, multivariate approaches to self-report and brain measures, and careful consideration of test-retest reliability. In addition, we should leave behind simplistic explanations of mindfulness, as there are many ways to be mindful, and leave behind simplistic explanations of the brain, as distributed networks of brain areas support mindfulness.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Exploring the neural basis of non-invasive prehabilitation in brain tumour patients: An fMRI-based case report of language network plasticity.

Primary brain neoplasms are associated with elevated mortality and morbidity rates. Brain tumour surgery aims to achieve maximal tumour resection while minimizing damage to healthy brain tissue. Research on Neuromodulation Induced Cortical Prehabilitation (NICP) has highlighted the potential, before neurosurgery, of establishing new brain connections and transfer functional activity from one area of the brain to another. Nonetheless, the neural mechanisms underlying these processes, particularly in the context of space-occupying lesions, remain unclear. A patient with a left frontotemporoinsular tumour underwent a prehabilitation protocol providing 20 sessions of inhibitory non-invasive neuromodulation (rTMS and multichannel tDCS) over a language network coupled with intensive task training. Prehabilitation resulted in an increment of the distance between the tumour and the language network. Furthermore, enhanced functional connectivity within the language circuit was observed. The present innovative case-study exposed that inhibition of the functional network area surrounding the space-occupying lesion promotes a plastic change in the network's spatial organization, presumably through the establishment of novel functional pathways away from the lesion's site. While these outcomes are promising, prudence dictates the need for larger studies to confirm and generalize these findings.

Neurophenomenological Investigation of Mindfulness Meditation "Cessation" Experiences Using EEG Network Analysis in an Intensively Sampled Adept Meditator.

Mindfulness meditation is a contemplative practice that is informed by Buddhism. It has been proven effective for improving mental and physical health in clinical and non-clinical contexts. To date, mainstream dialogue and scientific research on mindfulness has focused primarily on short-term mindfulness training and applications of mindfulness for reducing stress. Understanding advanced mindfulness practice has important implications for mental health and general wellbeing. According to Theravada Buddhist meditation, a "cessation" event is a dramatic experience of profound clarity and equanimity that involves a complete discontinuation in experience, and is evidence of mastery of mindfulness meditation. Thirty-seven cessation events were captured in a single intensively sampled advanced meditator (over 6,000 h of retreat mindfulness meditation training) while recording electroencephalography (EEG) in 29 sessions between November 12, 2019 and March 11, 2020. Functional connectivity and network integration were assessed from 40 s prior to cessations to 40 s after cessations. From 21 s prior to cessations there was a linear decrease in large-scale functional interactions at the whole-brain level in the alpha band. In the 40 s following cessations these interactions linearly returned to prior levels. No modulation of network integration was observed. The decrease in whole-brain functional connectivity was underlain by frontal to left temporal and to more posterior decreases in connectivity, while the increase was underlain by wide-spread increases in connectivity. These results provide neuroscientific evidence of large-scale modulation of brain activity related to cessation events that provides a foundation for future studies of advanced meditation.

Within-subject reliability of brain networks during advanced meditation: An intensively sampled 7 Tesla MRI case study.

Advanced meditation such as jhana meditation can produce various altered states of consciousness (jhanas) and cultivate rewarding psychological qualities including joy, peace, compassion, and attentional stability. Mapping the neurobiological substrates of jhana meditation can inform the development and application of advanced meditation to enhance well-being. Only two prior studies have attempted to investigate the neural correlates of jhana meditation, and the rarity of adept practitioners has largely restricted the size and extent of these studies. Therefore, examining the consistency and reliability of observed brain responses associated with jhana meditation can be valuable. In this study, we aimed to characterize functional magnetic resonance imaging (fMRI) reliability within a single subject over repeated runs in canonical brain networks during jhana meditation performed by an adept practitioner over 5 days (27 fMRI runs) inside an ultra-high field 7 Tesla MRI scanner. We found that thalamus and several cortical networks, that is, the somatomotor, limbic, default-mode, control, and temporo-parietal, demonstrated good within-subject reliability across all jhanas. Additionally, we found that several other relevant brain networks (e.g., attention, salience) showed noticeable increases in reliability when fMRI measurements were adjusted for variability in self-reported phenomenology related to jhana meditation. Overall, we present a preliminary template of reliable brain areas likely underpinning core neurocognitive elements of jhana meditation, and highlight the utility of neurophenomenological experimental designs for better characterizing neuronal variability associated with advanced meditative states.

Volitional mental absorption in meditation: Toward a scientific understanding of advanced concentrative absorption meditation and the case of jhana.

Meditation has been integral to human culture for millennia, deeply rooted in various spiritual and contemplative traditions. While the field of contemplative science has made significant steps toward understanding the effects of meditation on health and well-being, there has been little study of advanced meditative states, including those achieved through intense concentration and absorption. We refer to these types of states as advanced concentrative absorption meditation (ACAM), characterized by absorption with the meditation object leading to states of heightened attention, clarity, energy, effortlessness, and bliss. This review focuses on a type of ACAM known as jhana (ACAM-J) due to its well-documented history, systematic practice approach, recurring phenomenological themes, and growing popularity among contemplative scientists and more generally in media and society. ACAM-J encompasses eight layers of deep concentration, awareness, and internal experiences. Here, we describe the phenomenology of ACAM-J and present evidence from phenomenological and neuroscientific studies that highlight their potential applications in contemplative practices, psychological sciences, and therapeutics. We additionally propose theoretical ACAM-J frameworks grounded in current cognitive neuroscientific understanding of meditation and ancient contemplative traditions. We aim to stimulate further research on ACAM more broadly, encompassing advanced meditation including meditative development and meditative endpoints. Studying advanced meditation including ACAM, and specific practices such as ACAM-J, can potentially revolutionize our understanding of consciousness and applications for mental health.

Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior.

BACKGROUND: Neuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior. METHODS: We used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures. RESULTS: Significant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = -3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = -3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits. CONCLUSIONS: The findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.

Development of a digital intervention for psychedelic preparation (DIPP).

Psychedelic substances induce profound alterations in consciousness. Careful preparation is therefore essential to limit adverse reactions, enhance therapeutic benefits, and maintain user safety. This paper describes the development of a self-directed, digital intervention for psychedelic preparation. Drawing on elements from the UK Medical Research Council (MRC) framework for developing complex interventions, the design was informed by a four-factor model of psychedelic preparedness, using a person-centred approach. Our mixed-methods investigation consisted of two studies. The first involved interviews with 19 participants who had previously attended a 'high-dose' psilocybin retreat, systematically exploring their preparation behaviours and perspectives on the proposed intervention. The second study engaged 28 attendees of an ongoing psilocybin retreat in co-design workshops, refining the intervention protocol using insights from the initial interviews. The outcome is a co-produced 21-day digital course (Digital Intervention for Psychedelic Preparation (DIPP)), that is organised into four modules: Knowledge-Expectation, Psychophysical-Readiness, Safety-Planning, and Intention-Preparation. Fundamental components of the course include daily meditation practice, supplementary exercises tied to the weekly modules, and mood tracking. DIPP provides a comprehensive and scalable solution to enhance psychedelic preparedness, aligning with the broader shift towards digital mental health interventions.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Intensive whole-brain 7T MRI case study of volitional control of brain activity in deep absorptive meditation states.

Jhanas are profound states of mind achieved through advanced meditation, offering valuable insights into the nature of consciousness and tools to enhance well-being. Yet, its neurophenomenology remains limited due to methodological difficulties and the rarity of advanced meditation practitioners. We conducted a highly exploratory study to investigate the neurophenomenology of jhanas in an intensively sampled adept meditator case study (4 hr 7T fMRI collected in 27 sessions) who performed jhana meditation and rated specific aspects of experience immediately thereafter. Linear mixed models and correlations were used to examine relations among brain activity and jhana phenomenology. We identified distinctive patterns of brain activity in specific cortical, subcortical, brainstem, and cerebellar regions associated with jhana. Furthermore, we observed correlations between brain activity and phenomenological qualities of attention, jhanic qualities, and narrative processing, highlighting the distinct nature of jhanas compared to non-meditative states. Our study presents the most rigorous evidence yet that jhana practice deconstructs consciousness, offering unique insights into consciousness and significant implications for mental health and well-being.

Investigation of advanced mindfulness meditation "cessation" experiences using EEG spectral analysis in an intensively sampled case study.

Mindfulness meditation is a contemplative practice informed by Buddhism that targets the development of present-focused awareness and non-judgment of experience. Interest in mindfulness is burgeoning, and it has been shown to be effective in improving mental and physical health in clinical and non-clinical contexts. In this report, for the first time, we used electroencephalography (EEG) combined with a neurophenomenological approach to examine the neural signature of "cessation" events, which are dramatic experiences of complete discontinuation in awareness similar to the loss of consciousness, which are reported to be experienced by very experienced meditators, and are proposed to be evidence of mastery of mindfulness meditation. We intensively sampled these cessations as experienced by a single advanced meditator (with over 23,000 h of meditation training) and analyzed 37 cessation events collected in 29 EEG sessions between November 12, 2019, and March 11, 2020. Spectral analyses of the EEG data surrounding cessations showed that these events were marked by a large-scale alpha-power decrease starting around 40 s before their onset, and that this alpha-power was lowest immediately following a cessation. Region-of-interest (ROI) based examination of this finding revealed that this alpha-suppression showed a linear decrease in the occipital and parietal regions of the brain during the pre-cessation time period. Additionally, there were modest increases in theta power for the central, parietal, and right temporal ROIs during the pre-cessation timeframe, whereas power in the Delta and Beta frequency bands were not significantly different surrounding cessations. By relating cessations to objective and intrinsic measures of brain activity (i.e., EEG power) that are related to consciousness and high-level psychological functioning, these results provide evidence for the ability of experienced meditators to voluntarily modulate their state of consciousness and lay the foundation for studying these unique states using a neuroscientific approach.

Cessations of consciousness in meditation: Advancing a scientific understanding of nirodha samapatti.

Absence of consciousness can occur due to a concussion, anesthetization, intoxication, epileptic seizure, or other fainting/syncope episode caused by lack of blood flow to the brain. However, some meditation practitioners also report that it is possible to undergo a total absence of consciousness during meditation, lasting up to 7 days, and that these "cessations" can be consistently induced. One form of extended cessation (i.e., nirodha samapatti) is thought to be different from sleep because practitioners are said to be completely impervious to external stimulation. That is, they cannot be 'woken up' from the cessation state as one might be from a dream. Cessations are also associated with the absence of any time experience or tiredness, and are said to involve a stiff rather than a relaxed body. Emergence from meditation-induced cessations is said to have profound effects on subsequent cognition and experience (e.g., resulting in a sudden sense of clarity, openness, and possibly insights). In this paper, we briefly outline the historical context for cessation events, present preliminary data from two labs, set a research agenda for their study, and provide an initial framework for understanding what meditation induced cessation may reveal about the mind and brain. We conclude by integrating these so-called nirodha and nirodha samapatti experiences-as they are known in classical Buddhism-into current cognitive-neurocomputational and active inference frameworks of meditation.

Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS.

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

BDNF rs6265 Met carriers with alcohol use disorder show greater age-related decline of N-acetylaspartate in left dorsolateral prefrontal cortex.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is implicated in neuronal and glial cell growth and differentiation, synaptic plasticity, and apoptotic mechanisms. A single-nucleotide polymorphism of the BDNF rs6265 gene may contribute to the pattern and magnitude of brain metabolite abnormalities apparent in those with an Alcohol Use Disorder (AUD). We predicted that methionine (Met) carriers would demonstrate lower magnetic resonance spectroscopy (MRS) measures of N-acetylaspartate level (NAA) and greater age-related decline in NAA than valine (Val) homozygotes. METHODS: Veterans with AUD (n=95; 46±12 years of age, min = 25, max = 71) were recruited from VA Palo Alto residential treatment centers. Single voxel MRS, at 3 Tesla, was used to obtain NAA, choline (Cho) and creatine (Cr) containing compounds from the left dorsolateral prefrontal cortex (DLPFC). Metabolite spectra were fit with LC Model and NAA and Cho were standardized to total Cr level and NAA was also standardized to Cho. RESULTS: Val/Met (n=35) showed markedly greater age-related decline in left DLPFC NAA/Cr level than Val/Val (n=60); no differences in mean metabolite levels were observed between Val/Met and Val/Val. Val/Met demonstrated greater frequency of history of MDD and higher frequency of cannabis use disorder over 12 months prior to study. CONCLUSIONS: The greater age-related decline in left DLPFC NAA/Cr and the higher frequency of MDD history and Cannabis Use disorder in BDNF rs6265 Met carriers with AUD are novel and may have implications for non-invasive brain stimulation targeting the left DLFPC and other psychosocial interventions typically utilized in the treatment of AUD.

Real-Time Semi-Automated and Automated Voxel Placement using fMRI Targets for Repeated Acquisition Magnetic Resonance Spectroscopy.

BACKGROUND: Currently, magnetic resonance spectroscopy (MRS) is dependent on the investigative team to manually prescribe, or demarcate, the desired tissue volume-of-interest. The need for a new method to automate precise voxel placements is warranted to improve the utility and interpretability of MRS data. NEW METHOD: We propose and validate robust and real-time methods to automate MRS voxel placement using functionally defined coordinates within the prefrontal cortex. Data were collected and analyzed using two independent prospective studies: 1) two independent imaging days with each consisting of a multi-session sandwich design (MRS data only collected on one of the days determined based on scan time) and 2) a longitudinal design. Participants with fibromyalgia syndrome (N = 50) and major depressive disorder (N = 35) underwent neuroimaging. MRS acquisitions were acquired at 3-tesla. Evaluation of the reproducibility of spatial location and tissue segmentation was assessed for: 1) manual, 2) semi-automated, and 3) automated voxel prescription approaches RESULTS: Variability of voxel grey and white matter tissue composition was reduced using automated placement protocols. Spatially, post- to pre-voxel center-of-gravity distance was reduced and voxel overlap increased significantly across datasets using automated compared to manual procedures COMPARISON WITH EXISTING METHODS: Manual prescription, the current standard in the field, can produce inconsistent data across repeated acquisitions. Using automated voxel placement, we found reduced variability and more consistent voxel placement across multiple acquisitions CONCLUSIONS: These results demonstrate the within subject reliability and reproducibility of a method for reducing variability introduced by spatial inconsistencies during MRS acquisitions. The proposed method is a meaningful advance toward improved consistency of MRS data in neuroscience and can be utilized for multi-session and longitudinal studies.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.