{"id":"50a55c6f-ab05-4869-9bae-6b9cd8096bfa","arxiv_id":"2411.19922","paper_version":1,"verdict":"REJECT","confidence":"LOW","novelty_score":2.0,"correctness_risk":"high","formal_verification":"none","parameter_count":5,"one_line_summary":"A descriptive EEG-fMRI connectivity analysis that finds modular organization in sensory and default-mode networks, but lacks statistical reporting and reproducibility artifacts.","lead":"This paper combines EEG and fMRI recordings to map how brain connectivity changes over time. It reports modular organization in sensory and default-mode networks, but gives no quantitative results, code, or data to support the claims.","discovery_kind":"incremental","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim is not testable as written because the method text merges two incompatible paradigms, and the Results section reports no statistics that would connect the claimed findings to a specific dataset.","rationale":"The reader's verdict is REJECT with low confidence, based on missing statistics, internal inconsistency, and non-verifiability. My stress-test identifies the experimental-paradigm ambiguity as the single most load-bearing concern because it undermines the connection between the described methods and the reported results. This concern is closely related to the reader's rationale, which explicitly notes the abrupt transition from resting-state to neurofeedback motor-imagery text, but the reader's formal weakest_assumption focuses on HRF convolution and ICN selection. Those assumptions are secondary: if the dataset itself is ambiguous, no amount of correct HRF modeling or ICN selection can salvage the central claim until the data origin is resolved. The proposed concrete test (re-running the pipeline on each paradigm separately) would settle whether the claimed findings are genuine or an artifact of conflating datasets. Since this concern reinforces, rather than redirects, the reader's rejection, I recommend leaving the verdict unchanged. No ad hominem is implied; the issue is internal consistency and reproducibility, not author intent.","tokens_in":5762,"tokens_out":2859,"duration_ms":28880,"concrete_test":"Request the dataset and analysis code from the authors and re-run the full pipeline separately on (a) the resting eyes-open/eyes-closed sessions and (b) the motor-imagery neurofeedback sessions, using the same 54 ICNs, HRF convolution, sliding-window parameters (L=20 TRs, step 1 TR), and modularity-based state detection for each dataset. Then compare the resulting static connectivity maps and identified connectivity states. If the modular organization and 30–60 s state identification appear only in one dataset, the published claim conflates paradigms and the Results section must be reanalyzed. If they appear in both, the inconsistency is resolved, though separate statistical reporting would still be required.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The manuscript's central claim—that modular organization in sensory/default-mode ICNs and cognitive-state identification from 30–60 s windows are empirically demonstrated—depends on a single coherent dataset being analyzed. Section II.B describes a resting-state design: eyes-closed then eyes-open, each 8.5 min (256 TRs at TR=2 s). Section II.E, after describing ICA of these resting data, abruptly states that each participant underwent three motor-imagery neurofeedback sessions of 320 s each, with 20 s rest/motor blocks, and that data from 13 participants were shown as 1D and 12 as 2D. The graph construction in Section II.F does not state which of these datasets feeds the correlation matrices; it only refers to 'the matrix EFEFEF', which is undefined. The static and dynamic EEG-fMRI graphs (84 nodes) are computed from 256 time points, matching the resting design, but the neurofeedback passages imply additional data that are not reconciled. If the reported Figure 4B and sliding-window states were generated from the motor-imagery sessions instead of the resting eyes-open/eyes-closed sessions, then the conclusions about 'visual states' and the 30–60 s timeframe would apply to a different task than the one described in the Methods. The Results section contains no statistical values, effect sizes, or variance measures, so one cannot infer which dataset produced the displayed averages. This is more load-bearing than the HRF/ICN-assumption concern because it cannot even be checked without first knowing the experimental origin of the data.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript reports an EEG-fMRI connectivity study that constructs static and dynamic graph-theoretic brain networks from 30 EEG channels and 54 fMRI ICA components across five frequency bands in eyes-open and eyes-closed conditions. It claims to reveal modular organization in sensory and default-mode networks and to show that cognitive states can be identified from 30-60 s windows of data. The methods describe sliding-window correlation analysis, graph metrics, and a modularity-based state-detection procedure, but the results section contains only figure descriptions and no statistical values.","tokens_in":6018,"tokens_out":5517,"duration_ms":51414,"significance":"If the reported findings were supported, the proposed multimodal graph framework could be a useful contribution to EEG-fMRI connectivity research, particularly the idea of using short sliding windows to track cognitive-state-related network reconfiguration. However, the manuscript as written does not substantiate these claims: no test statistics, p-values, effect sizes, or variance measures are reported, and the central dataset is not unambiguously identified. The temporal claim about 30-60 s windows rests on a choice made from the same assumption, making it circular. No code or data are provided to support reproducibility. The significance of the work is therefore currently unverifiable.","major_comments":[{"comment":"The manuscript does not identify which dataset is used for the connectivity analysis. Section II.B describes only a resting-state design with 8.5 minutes of eyes-closed followed by 8.5 minutes of eyes-open, while Section II.E abruptly introduces three 320-second motor-imagery neurofeedback sessions per participant with alternating 20-second rest and motor blocks. Section II.F then refers to a matrix 'EFEFEF' of dimensions 256×84 without defining this matrix or stating whether it comes from the resting sessions or the neurofeedback sessions. This ambiguity is load-bearing because the abstract and conclusions make claims about cognitive and visual states, and the reader cannot determine whether the reported graphs and states were computed from resting data, motor-imagery data, or both.","section":"II.B, II.E, II.F"},{"comment":"No inferential statistics are reported anywhere in the results. Section II.F states that a 5×2 repeated-measures ANOVA and paired t-tests were conducted on the static and dynamic measurements, but Section III reports no F values, p-values, effect sizes, confidence intervals, or measures of variability. Claims such as 'modular organization in sensory systems and default mode regions' and 'structural differences across five frequency bands' are asserted based on visual inspection of Figure 4B. These are central empirical claims of the paper, and their absence of statistical support means the findings cannot be evaluated.","section":"III.A, II.F"},{"comment":"The central temporal claim is circular. The sliding-window length is set to 20 TRs (40 seconds) in Section II.F based on prior research 'indicating that cognitive states can be accurately identified with data from short periods of 30 to 60 seconds.' The conclusion then presents as a finding that 'cognitive states can be effectively identified through short-duration data, specifically within the 30-60 second timeframe.' Because the window length was chosen from the very assumption the paper claims to demonstrate, the result does not provide independent evidence for the 30-60 second claim.","section":"II.F, IV"},{"comment":"The connectivity-state analysis described in Section II.H is not reported in the results. The methods promise detection of distinct connectivity states via modularity of a time-window correlation matrix, but the results section does not state the number of states found, their module compositions, their temporal durations, or any validation against null models. The abstract's assertion of 'distinct connectivity states' is therefore unsupported by any presented quantitative output.","section":"II.H, III"},{"comment":"Key computational details are missing, preventing reproducibility. The matrix 'EFEFEF' is never defined; the construction of the EEG spectral power time series is not specified (e.g., how band power is computed, what window is used, and how the 5 kHz EEG data are matched to the 2-second TR timescale); the HRF convolution parameters are not given; and the modularity algorithm used in Section II.H is not named or parameterized. Without these details, even the described pipeline cannot be reimplemented or checked.","section":"II.F"}],"minor_comments":[{"comment":"The section heading 'Participator' contains a typo; it should read 'Participants.'","section":"II.A"},{"comment":"Before equation (1), the text contains 'RRR' where the correlation matrix R is presumably meant, and the displayed equations are not rendered correctly, making it difficult to read the definitions of positive and negative weighted connections.","section":"II.F"},{"comment":"The text says the static maps were computed 'for more than 25 participants,' but Section II.A reports exactly 25 participants. This numerical inconsistency should be corrected.","section":"III.A"},{"comment":"Figure 2 is described as the 'dual-modal neurofeedback metaphor presented during the conference,' but it is never integrated into the analysis, and the text does not explain how the 1D versus 2D display conditions relate to the connectivity results.","section":"II.E, Figure 2"},{"comment":"Several references, such as [3], [4], [6], and [8], appear to be about layer-specific fMRI or unrelated topics and are not clearly cited in the relevant parts of the text; the reference list should be aligned with the actual citations.","section":"References"}],"recommendation":"reject","confidential_remarks":"The manuscript has a severe internal inconsistency between the resting-state design and the neurofeedback sessions, and the results section contains no numerical evidence for any of the central claims. In my view, the circularity of the 30-60 second window argument is not a presentational flaw but a fundamental issue with the interpretation of the analysis, and the missing statistics mean the paper cannot be evaluated as an empirical study. Even with a thorough revision, the authors would need to clarify the dataset, report the promised statistical tests, and reframe the temporal claim as an assumption rather than a finding. I therefore recommend rejection rather than major revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Dear colleague,\n\nPunchline: this manuscript doesn't yet support its own conclusions. The method section describes a resting-state eyes-open/eyes-closed design, then abruptly introduces motor-imagery neurofeedback sessions with 1D/2D feedback, and the graph construction never says which dataset produced the reported correlations. The Results contain no test statistics, effect sizes, or variance measures—only references to figures. As written, the central claims about modular organization and the 30–60 second cognitive-state window cannot be checked.\n\nWhat's good: the graph-theoretic metrics (connection strength, clustering coefficient, global efficiency) are standard and clearly defined, and the idea of convolving EEG spectral power with an HRF before correlating with fMRI time courses is a reasonable, established approach. The authors also acknowledge that their findings align with prior work, so they aren't overclaiming novelty.\n\nWhere it falls apart: the paradigm inconsistency is load-bearing. If the sliding-window states came from the motor-imagery sessions rather than the resting-state sessions, then the \"visual states\" and 30–60 s findings apply to a different task than the one described in the Methods. That alone makes the empirical section uninterpretable. On top of that, the reference list is sloppy—several citations don't support the sentences they're attached to (e.g., [5] is a Bayesian FSL paper, not EEG-fMRI connectivity). The window length justification partly leans on the authors' own earlier work, which is minor by comparison but still a soft spot. No data or code are provided, so even the preprocessing pipeline (ICA, ICN selection) can't be independently checked.\n\nWho this is for: maybe a methods class as an example of a multimodal graph-analysis pipeline, but not as a citable result. This reads like an early draft where two separate analyses were pasted together. I would not send it to peer review in its current form; I'd return it with a request to specify the dataset unambiguously, add proper statistics, and fix the references.\n\nRecommendation: desk reject with encouragement to resubmit after major revision.\n\nBest,\n[You]","headline":"The methods text merges two incompatible experiments and the Results report no statistics, so the central claims aren't verifiable as written.","tokens_in":6552,"tokens_out":2370,"would_cite":false,"duration_ms":21250,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that simultaneous EEG-fMRI recordings, analyzed in 40-second sliding windows and clustered by modularity, reveal distinct connectivity states dominated by sensory systems and the default mode network, supporting the…","keywords":["EEG-fMRI integration","dynamic functional connectivity","intrinsic connectivity networks","graph theory","sliding window","cognitive states","default mode network","sensory systems"],"falsifier":"Recompute the dynamic connectivity states after replacing the fMRI component time courses with phase-randomized surrogate signals that preserve each time course's autocorrelation; if the same modular states in sensory and default-mode networks emerge from the surrogates, the claimed states are a statistical artifact rather than neural connectivity. A second check: repeat the ICN selection from the same 100 ICA components with an independent rater or an automated classifier and test whether the modular organization and the 30-60 second state separation survive.","tokens_in":5525,"feed_emoji":"🧠","tokens_out":5843,"duration_ms":47381,"temperature":0.7,"pith_summary":"This paper tries to show that simultaneous EEG and fMRI recordings can be combined into a single connectivity graph whose dynamic changes track cognitive state. The authors build static and sliding-window graphs connecting 30 EEG electrodes to 54 fMRI-derived intrinsic connectivity networks, then identify recurring connectivity states from the temporal pattern of these graphs. They report modular organization centered on sensory systems and the default mode network, with states recoverable from windows of about 30-60 seconds. If this holds, multimodal EEG-fMRI connectivity analysis could monitor cognitive state changes with practical short recordings.","feed_headline":"Modular brain states emerge from combined EEG and fMRI in 40-second windows","feed_subtitle":"40-second windows of simultaneous EEG-fMRI reveal sensory and default-mode networks shifting between connection states.","key_machinery":"The load-bearing object is the EEG-fMRI connectivity graph: an $84 \\times 84$ Pearson correlation matrix ($30$ EEG electrodes plus $54$ fMRI independent-component networks) computed per EEG frequency band, after convolving the EEG power time series with a standard hemodynamic response function. Dynamic analysis slides a $20$-TR ($40$ s) window across the data to produce $237$ time-varying graphs, from which node-level connection strength, clustering coefficient, and global efficiency are computed. A second correlation matrix over time windows is then clustered by a modularity algorithm, and each resulting module of time windows is averaged into a single representative connectivity state.","core_discovery":"The central claim is that EEG spectral power and fMRI intrinsic-connectivity-network time courses, when convolved with a hemodynamic response function and correlated in a sliding 40-second window, produce brain graphs whose modular structure is stable enough to define distinct connectivity states. Across eyes-open and eyes-closed resting conditions, the detected modules are dominated by sensory (visual, auditory, sensorimotor) systems and the default mode network, with anti-correlations between these broad regions. The paper further claims that these states align with earlier findings that cognitive states can be identified from short-duration data, specifically windows of 30-60 seconds.","pith_inferences":["Editorial inference: the 40-second window choice is justified by prior reports, but the paper does not directly compare window lengths; a natural extension would be to test windows from 10 to 60 seconds on the same data to see whether the 30-60 second claim is a plateau or a sharp optimum.","Editorial inference: since the pipeline operates on unlabeled eyes-open/eyes-closed data, applying it to task or clinical data with ground-truth state labels would test whether discovered states correspond to behavior, not just to recording condition.","Editorial inference: the reliance on a 1.5 T scanner and 30 scalp electrodes suggests the approach may be reproducible in lower-cost settings, which would be relevant for EEG-based neurofeedback if the fMRI nodes can be replaced by a template atlas.","Editorial inference: the paper's evidence for ICNs comes from group-level ICA, so individual-level state detection would require per-subject components; verifying that the modular states appear in single subjects would be a direct test of practical use."],"forward_implications":["Cognitive-state classification can be performed on roughly 40-second multimodal recordings, making EEG-fMRI connectivity analysis feasible for settings where long scans are impractical.","The modular separation between sensory systems and the default mode network appears in both static and dynamic EEG-fMRI graphs, suggesting a stable cross-modal backbone of brain organization.","Sliding-window graph metrics fluctuate in a low-frequency range, so dynamic connectivity changes are slow enough to be captured at this temporal resolution.","Because connectivity states are defined without a task label, the same pipeline could be applied to naturalistic or clinical recordings to discover state changes from data alone.","Anti-correlations between sensory and default-mode modules are preserved in the negative-correlation graph, indicating that the sign of EEG-fMRI coupling carries information about state."],"supporting_citations":[{"why":"Cited as the basis for applying graph-theory analysis, including the metrics used to quantify brain network connectivity.","marker":"[1]"},{"why":"Motivates the core premise that integrating EEG and fMRI offers a deeper understanding of brain dynamics and network topology.","marker":"[3]"},{"why":"Provides the empirical basis for treating low-frequency EEG connectivity as comparable to fMRI connectivity, justifying the correlation approach.","marker":"[5]"},{"why":"Demonstrates that simultaneous EEG-fMRI recordings can capture brain-state-dependent connectivity, supporting the feasibility of the dynamic analysis.","marker":"[7]"}],"fun_headline_variants":["EEG-fMRI coupling reveals brain's modular state shifts in 40-second windows","Dynamic EEG-fMRI maps link brain modularity to cognitive states","Sensory and default-mode networks emerge in EEG-fMRI state snapshots","Cognitive states track EEG-fMRI connectivity states within a minute","40-second EEG-fMRI windows capture distinct brain connectivity states"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole analysis depends on treating Pearson correlations between EEG spectral power (after hemodynamic convolution) and fMRI component time courses as genuine neural connectivity, and on the manual selection of 54 of 100 ICA components as an unbiased set of intrinsic connectivity networks; if those correlations or that selection are dominated by artifacts, the reported modular states would not reflect brain organization.","fun_headline_variants_meta":{"raw":{"variants":["EEG-fMRI coupling reveals brain's modular state shifts in 40-second windows","Dynamic EEG-fMRI maps link brain modularity to cognitive states","Sensory and default-mode networks emerge in EEG-fMRI state snapshots","Cognitive states track EEG-fMRI connectivity states within a minute","40-second EEG-fMRI windows capture distinct brain connectivity states"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000901,"raw_usage":{"total_tokens":3844,"prompt_tokens":875,"completion_tokens":2969,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":491,"completion_tokens_details":{"reasoning_tokens":2879}},"tokens_in":491,"tokens_out":2969,"duration_ms":19114,"temperature":1.0,"reasoning_tokens":2879,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T05:40:58.215486+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Recompute the dynamic connectivity states after replacing the fMRI component time courses with phase-randomized surrogate signals that preserve each time course's autocorrelation; if the same modular states in sensory and default-mode networks emerge from the surrogates, the claimed states are a statistical artifact rather than neural connectivity. A second check: repeat the ICN selection from the same 100 ICA components with an independent rater or an automated classifier and test whether the modular organization and the 30-60 second state separation survive.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Motivates the core premise that integrating EEG and fMRI offers a deeper understanding of brain dynamics and network topology."},{"cited_title":"NeuroImage, 45(S1): S173-S186","cited_arxiv_id":null,"evidence_quote":"Provides the empirical basis for treating low-frequency EEG connectivity as comparable to fMRI connectivity, justifying the correlation approach."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Demonstrates that simultaneous EEG-fMRI recordings can capture brain-state-dependent connectivity, supporting the feasibility of the dynamic analysis."}],"review_version":1}