Signal and Data Processing

fa تشخیص بهرۀ هوشی از روی تصاویر fMRI با استفاده از ترکیب شبکۀ حافظۀ کوتاه‌مدت بلند و منطق فازی Detection of intelligence from fMRI images using a combination of long short-term memory network (LSTM) and fuzzy logic (FL) مقالات پردازش تصویر Paper پژوهشي Research <div style="text-align: justify;">بهرۀ هوشی<a href="#_ftn1" name="_ftnref1" title="">[1]</a> از دیرباز موضوعی جذاب و مهم در روان‌شناسی و علوم شناختی بوده‌است. این مسئله یک معیار اساسی برای توانایی‌های شناختی یک فرد درنظر گرفته می‌شود و شامل جنبه‌های مختلف استدلال، حل مسئله، حافظه و توانایی کلی فکری است. باتوجه به اهمیت این موضوع، در این پژوهش، به تحلیل و توسعۀ یک مدل ترکیبی از شبکه‌های LSTM و منطق استنتاج فازی به‌منظور تشخیص بهرۀ هوشی با استفاده از تصاویر fMRI مغزی پرداخته‌ شده‌است. مدل پیشنهادی پژوهش حاضر با ترکیب قدرت یادگیری عمیق LSTM در درک الگوهای زمانی و قابلیت‌های استنتاج فازی در مدیریت عدم‌قطعیت، موفق به افزایش دقت در شناسایی سطح بهرۀ هوشی نسبت به روش‌های پایه شده‌است. نتایج آزمایش‌ها نشان داد که دقت مدل به‌طور معناداری بهتر از روش‌های سنتی بوده و با نرخ تشخیص بهرۀ هوشی 83 درصد بهتر از سایر روش‌ها جواب داده است. این نرخ نشان‌دهندۀ هم‌بستگی مثبت و درستی بین اندازه و نواحی انتخاب‌شدۀ مغز انسان و ضریب هوشی است.</div> <div>  <hr align="left" size="1" width="33%" > <div id="ftn1"><a href="#_ftnref1" name="_ftn1" title="">[1]</a> Intelligence Quotient(IQ)</div> </div>   <div> <div id="ftn1"> </div> </div> <div style="text-align: justify;">Intelligence Quotient (IQ) is a widely used indicator of cognitive ability and is commonly assessed through standardized psychometric tests. However, questionnaire-based IQ assessments may be impractical for certain populations (e.g., very young children) and can be influenced by non-neural factors. In recent years, neuroimaging has enabled the investigation of the relationship between brain structure/function and intelligence. Functional Magnetic Resonance Imaging (fMRI), due to its ability to capture brain activity patterns, provides an opportunity for building automated IQ estimation or IQ-level classification systems. Nevertheless, fMRI data are high-dimensional, noisy, and temporally complex, which motivates the use of deep learning models that can learn sequential dependencies. This study proposes a hybrid framework that combines Long Short-Term Memory (LSTM) networks with a fuzzy inference engine to improve robustness under uncertainty and noise. This research uses the ABIDE dataset, which provides resting-state fMRI data along with phenotypic information. The Full-Scale IQ (FIQ) is considered as the main intelligence criterion. Since FIQ is missing for a subset of subjects, it is computed from Verbal IQ (VIQ) and Performance IQ (PIQ) using the linear relation reported in the manuscript (Relation (1)). The pipeline consists of three main stages:</div> <ol> <li class="CxSpMiddle" style="text-align: justify;">Preprocessing: Skull stripping and preparation of brain data, followed by standard fMRI preprocessing steps including noise removal, normalization, realignment, smoothing, ROI selection using the AAL atlas, and extraction of ROI-wise temporal BOLD signals.</li> <li class="CxSpMiddle" style="text-align: justify;">Temporal Feature Learning (LSTM): The extracted ROI time-series are fed to a multi-layer LSTM to capture temporal dynamics and long-range dependencies. The learned latent representation is mapped to a normalized score qLSTMq_{text{LSTM}}qLSTM that summarizes the temporal evidence.</li> <li class="CxSpMiddle" style="text-align: justify;">Fuzzy Inference (TSK): To manage uncertainty in brain signals and improve decision stability, a first-order Takagi–Sugeno–Kang (TSK) fuzzy inference system is employed. The fuzzy system uses qLSTMq_{text{LSTM}}qLSTM along with additional normalized indices (e.g., connectivity and structural indicators defined in the manuscript) to infer the final IQ estimate/class. The rule-base (Table (2)) is used to combine evidence through weighted aggregation of membership degrees. For evaluation, the data are split into training and test sets, and K-fold cross-validation is used. Performance is reported using accuracy and class-wise F1-score (as illustrated in the corresponding figures), and the proposed method is compared against baseline architectures.</li> </ol> <div style="text-align: justify;">The experimental results indicate that the proposed hybrid LSTM–Fuzzy model achieves higher performance than the baseline methods implemented under the same experimental setting on ABIDE. In particular, the proposed approach reaches an accuracy of 83%, demonstrating a noticeable improvement over conventional deep models and hybrid baselines. The learning curves show a decreasing trend of training and testing errors across boosting/training rounds, suggesting stable convergence. In addition, the class-wise F1-score analysis confirms that the proposed method improves classification consistency across IQ-level categories. The improvements can be attributed to two complementary factors. First, LSTM effectively models the temporal variability of resting-state fMRI signals and captures sequential dependencies across ROI time-series. Second, fuzzy inference provides a principled mechanism to handle ambiguity and noise by translating numerical evidence into interpretable linguistic rules and aggregating them through membership degrees. This hybrid design increases robustness when the neuroimaging data contain uncertainty, which is common in multi-site resting-state datasets. A key practical aspect of this work is the consistent use of FIQ (reported or computed via Relation (1)), which ensures coherent labeling and evaluation across subjects. Limitations include dataset heterogeneity (scanner/site effects), computational complexity, and sensitivity to preprocessing choices. Future work may incorporate domain adaptation, transfer learning, or more advanced temporal encoders to improve generalization across acquisition sites. This study presents a hybrid deep learning and fuzzy logic framework for IQ detection/estimation from resting-state fMRI. By combining LSTM-based temporal feature learning with a TSK fuzzy inference engine, the proposed approach improves performance and robustness on the ABIDE dataset. The results suggest that integrating deep sequential modeling with fuzzy reasoning is an effective strategy for fMRI-based cognitive score analysis.</div> بهرۀ هوشی, یادگیری عمیق, تصاویر fMRI مغزی, شبکۀ LSTM, موتور استنتاج فازی. IQ, deep learning, brain fMRI images, LSTM network, fuzzy inference engine. 51 60 http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-2246-2&slc_lang=fa&sid=1 Sara Motamed سارا معتمد motamed.sarah@gmail.com Yes Assistant Professor, Department of Computer Engineering, FSh.C., Islamic Azad University, Fouman, Iran استادیار گروه کامپیوتر، واحد فومن و شفت، دانشگاه آزاد اسلامی، فومن، ایران Majid Eghdami Arabani مجید اقدامی عربانی eghdami@gmail.com No Master graduate, Department of Computer Engineering, FSh.C., Islamic Azad University, Fouman, Iran دانش‌‎آموختۀ کارشناسی ‌ارشد، گروه کامپیوتر، واحد فومن و شفت، دانشگاه آزاد اسلامی، فومن، ایران