The unique neural signature of your trip: Functional connectome fingerprints of subjective psilocybin experience

The emerging neuroscientific frontier of brain fingerprinting has recently established that human functional connectomes (FCs) exhibit fingerprint-like idiosyncratic features, which map onto heterogeneously distributed behavioral traits. Here, we harness brain-fingerprinting tools to extract FC features that predict subjective drug experience induced by the psychedelic psilocybin. Specifically, in neuroimaging data of healthy volunteers under the acute influence of psilocybin or a placebo, we show that, post psilocybin administration, FCs become more idiosyncratic owing to greater intersubject dissimilarity. Moreover, whereas in placebo subjects idiosyncratic features are primarily found in the frontoparietal network, in psilocybin subjects they concentrate in the default mode network (DMN). Crucially, isolating the latter revealed an FC pattern that predicts subjective psilocybin experience and is characterized by reduced within-DMN and DMN-limbic connectivity, as well as increased connectivity between the DMN and attentional systems. Overall, these results contribute to bridging the gap between psilocybin-mediated effects on brain and behavior, while demonstrating the value of a brain-fingerprinting approach to pharmacological neuroimaging.

Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

Brain fingerprinting field study on major, terrorist crimes supports the brain fingerprinting scientific standards hypothesis: classification concealed information test with P300 and P300-MERMER succeeds; comparison CIT fails

We conducted (I) 18 event-related potential (ERP) field tests to detect concealed information regarding major terrorist crimes and other real-world crimes and (II) 5 ERP tests regarding participation in a classified counterterrorism operation. This study is a test of the brain fingerprinting scientific standards hypothesis: that a specific set of methods for event-related potential (ERP) concealed information tests (CIT) known as the brain fingerprinting scientific standards provide the sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence for individual determinations of whether the tested information is stored in each subject's brain. All previous published results in all laboratories are compatible with this hypothesis. We recorded P300 and P300-MERMER ERP responses to visual text stimuli of three types: targets contain known information, irrelevants contain unknown/irrelevant information, and probes contain the situation-relevant information to be tested, known only to the perpetrator and investigators. Classification CIT produced significantly better results than comparison CIT, independent of classification criteria. Classification CIT had 0% error rate; comparison CIT had 6% error rate. As in previous studies, classification-CIT median statistical confidences were approximately 99%, whereas comparison CIT statistical confidences were no better than chance for information-absent (IA) subjects (who did not know the tested information). Over half of the comparison-CIT IA determinations were invalid due to a less-than-chance computed probability of being correct. Experiment (I) results for median statistical confidence: Classification CIT, IA subjects: 98.6%; information-present (IP) subjects (who know the tested information): 99.9%; comparison CIT, IA subjects: 48.7%; IP subjects: 99.5%. Experiment (II) results (Classification CIT): error rate 0%, median statistical confidence 96.6%. Countermeasures had no effect on the classification CIT. These results, like all previous results in our laboratory and all others, support the brain fingerprinting scientific standards hypothesis and indicate that the classification CIT is a necessary condition for a reliable, accurate, and valid brainwave-based CIT. The comparison CIT, by contrast, produces high error rates and IA statistical confidences no better than chance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09795-1.

Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

Cortical responses to noninvasive perturbations enable individual brain fingerprinting

BACKGROUND

In recent years, it has become increasingly apparent that characterizing individual brain structure, connectivity and dynamics is essential for understanding brain function in health and disease. However, the majority of neuroimaging and brain stimulation research has characterized human brain function by averaging measurements from groups of subjects and providing population-level inferences. External perturbations applied directly to well-defined brain regions can reveal distinctive information about the state, connectivity and dynamics of the human brain at the individual level.

OBJECTIVES

In a series of studies, we aimed to characterize individual brain responses to MRI-guided transcranial magnetic stimulation (TMS), and explore the reproducibility of the evoked effects, differences between brain regions, and their individual specificity.

METHODS

In the first study, we administered single pulses of TMS to both anatomically (left dorsolateral prefrontal cortex- 'L-DLPFC', left Intra-parietal lobule- 'L-IPL) and functionally (left motor cortex- 'L-M1', right default mode network- 'R-DMN, right dorsal attention network- 'R-DAN') defined cortical nodes in the frontal, motor, and parietal regions across two identical sessions spaced one month apart in 24 healthy volunteers. In the second study, we extended our analyses to two independent data sets (n = 10 in both data sets) having different sham-TMS protocols.

RESULTS

In the first study, we found that perturbation-induced cortical propagation patterns are heterogeneous across individuals but highly reproducible within individuals, specific to the stimulated region, and distinct from spontaneous activity. Most importantly, we demonstrate that by assessing the spatiotemporal characteristics of TMS-induced brain responses originating from different cortical regions, individual subjects can be identified with perfect accuracy. In the second study, we demonstrated that subject specificity of TEPs is generalizable across independent data sets and distinct from non-transcranial neural responses evoked by sham-TMS protocols.

CONCLUSIONS

Perturbation-induced brain responses reveal unique "brain fingerprints" that reflect causal connectivity dynamics of the stimulated brain regions, and may serve as reliable biomarkers of individual brain function.

Multi-modal brain fingerprinting: A manifold approximation based framework

This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting-state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis.

Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data

Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).

Brain fingerprinting field studies comparing P300-MERMER and P300 brainwave responses in the detection of concealed information

Brain fingerprinting detects concealed information stored in the brain by measuring brainwave responses. We compared P300 and P300-MERMER event-related brain potentials for error rate/accuracy and statistical confidence in four field/real-life studies. 76 tests detected presence or absence of information regarding (1) real-life events including felony crimes; (2) real crimes with substantial consequences (either a judicial outcome, i.e., evidence admitted in court, or a $100,000 reward for beating the test); (3) knowledge unique to FBI agents; and (4) knowledge unique to explosives (EOD/IED) experts. With both P300 and P300-MERMER, error rate was 0 %: determinations were 100 % accurate, no false negatives or false positives; also no indeterminates. Countermeasures had no effect. Median statistical confidence for determinations was 99.9 % with P300-MERMER and 99.6 % with P300. Brain fingerprinting methods and scientific standards for laboratory and field applications are discussed. Major differences in methods that produce different results are identified. Markedly different methods in other studies have produced over 10 times higher error rates and markedly lower statistical confidences than those of these, our previous studies, and independent replications. Data support the hypothesis that accuracy, reliability, and validity depend on following the brain fingerprinting scientific standards outlined herein.

A comment on Farwell (2012): brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials

In a recent issue of Cognitive Neurodynamics Farwell (Cogn Neurodyn 6:115-154, 2012) published a comprehensive tutorial review of the use of Event Related Brain Potentials (ERP) in the detection of concealed information. Farwell's review covered much of his own work employing his "brain fingerprinting" technology. All his work showed a 100 % accuracy rate in detecting concealed information. We argue in this comment that Farwell (Cogn Neurodyn 6:115-154, 2012) is misleading and misrepresents the scientific status of brain fingerprinting technology.

Brain fingerprinting: let's focus on the science-a reply to Meijer, Ben-Shakhar, Verschuere, and Donchin

Farwell in Cogn Neurodyn 6:115-154, (2012) reviewed all research on brainwave-based detection of concealed information published in English, including the author's laboratory and field research. He hypothesized that specific methods are sufficient to obtain less than 1 % error rate and high statistical confidence, and some of them are necessary. Farwell proposed 20 brain fingerprinting scientific standards embodying these methods. He documented the fact that all previous research and data are compatible with these hypotheses and standards. Farwell explained why failure to meet these standards resulted in decrements in performance of other, alternative methods. Meijer et al. criticized Farwell in Cogn Neurodyn 6:115-154, (2012) and Farwell personally. The authors stated their disagreement with Farwell's hypotheses, but did not cite any data that contradict the three hypotheses, nor did they propose alternative hypotheses or standards. Meijer et al. made demonstrable misstatements of fact, including false ad hominem statements about Farwell, and impugned Farwell's motives and character. We provide supporting evidence for Farwell's three hypotheses, clarify several issues, correct Meijer et al.'s misstatements of fact, and propose that the progress of science is best served by practicing science: designing and conducting research to test and as necessary modify the proposed hypotheses and standards that explain the existing data.

Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials

Brain fingerprinting (BF) detects concealed information stored in the brain by measuring brainwaves. A specific EEG event-related potential, a P300-MERMER, is elicited by stimuli that are significant in the present context. BF detects P300-MERMER responses to words/pictures relevant to a crime scene, terrorist training, bomb-making knowledge, etc. BF detects information by measuring cognitive information processing. BF does not detect lies, stress, or emotion. BF computes a determination of "information present" or "information absent" and a statistical confidence for each individual determination. Laboratory and field tests at the FBI, CIA, US Navy and elsewhere have resulted in 0% errors: no false positives and no false negatives. 100% of determinations made were correct. 3% of results have been "indeterminate." BF has been applied in criminal cases and ruled admissible in court. Scientific standards for BF tests are discussed. Meeting the BF scientific standards is necessary for accuracy and validity. Alternative techniques that failed to meet the BF scientific standards produced low accuracy and susceptibility to countermeasures. BF is highly resistant to countermeasures. No one has beaten a BF test with countermeasures, despite a $100,000 reward for doing so. Principles of applying BF in the laboratory and the field are discussed.