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\begin{document}
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\title{BETA: Resting-state fMRI Biotypes for tDCS Efficacy in Anxiety among Older Adults at Risk for Alzheimer's Disease}
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\author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and
Second Author\inst{2,3}\orcidID{1111-2222-3333-4444} \and
Third Author\inst{3}\orcidID{2222--3333-4444-5555}}
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\authorrunning{F. Author et al.}
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\institute{Princeton University, Princeton NJ 08544, USA \and
Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, Germany
\email{lncs@springer.com}\\
\url{http://www.springer.com/gp/computer-science/lncs} \and
ABC Institute, Rupert-Karls-University Heidelberg, Heidelberg, Germany\\
\email{\{abc,lncs\}@uni-heidelberg.de}}

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\author{Anonymized Authors}  %% Added for anonymized MICCAI 2025 submission
\authorrunning{Anonymized Author et al.}
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    \email{email@anonymized.com}}

    
\maketitle              % typeset the header of the contribution
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\begin{abstract}
%The abstract should briefly summarize the contents of the paper in 150--250 words.  If you are to include a link to your Repository, please make sure it is anonymized for the double-blind review phase.

Anxiety is usually gauged by self‑report, yet a single symptom level can reflect disparate neural circuitry. In Alzheimer’s disease and related dementias (ADRD) this heterogeneity becomes a barrier to effective neuromodulation: some patients may benefit from transcranial direct‑current stimulation (tDCS), while others may not. To overcome this obstacle, we introduced BETA (Biotypes for tDCS Efficacy in Anxiety), a data‑driven pipeline that uses resting‑state fMRI functional connectivity to derive anxiety subtypes that are intrinsically linked to tDCS response. A transformer‑based variational autoencoder compresses high‑dimensional connectivity into a 50‑dimensional latent embedding that emphasizes networks implicated in cognitive aging and anxiety. A deep‑embedded clustering loss, regularized by a clinically informed term that pulls together individuals who exhibit similar post‑tDCS anxiety change, yields five distinct subtypes. Across all subtypes, disrupted coupling between sensory‑processing and higher‑order cognitive regions emerges as a common hallmark. Crucially, one cluster is resistant to frontal‑lobe tDCS, whereas two clusters demonstrate significant anxiety reduction following stimulation. The responsive subtypes are defined by strengthened connectivity between the lateral occipital cortex—superior division (sLOC) and medial frontal cortex (MedFC), and between sLOC and the intracalcarine cortex (ICC). BETA demonstrates that fMRI‑based subtyping can directly identify which patients are likely to benefit from tDCS, providing a concrete roadmap for precision psychiatry in ADRD and facilitating tailored therapeutic strategies for anxiety. 


\keywords{Anxiety \and tDCS \and ADRD \and functional MRI \and biotypes}
% Authors must provide keywords and are not allowed to remove this Keyword section.

\end{abstract}
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\section{Introduction}

Anxiety is one of the most frequently reported neuropsychiatric symptoms in individuals with Alzheimer’s disease (AD) and related dementias (ADRD) \cite{ferretti2001anxiety,porter2003frequency,patel2021prevalence}.  Beyond its symptomatic burden, anxiety has been implicated as both a risk factor for, and a catalyst of, disease progression in AD \cite{chemerinski1998prevalence,becker2018anxiety}.  Consequently, early and precise anxiety interventions may attenuate neurodegeneration and improve quality of life in this vulnerable population.

Current clinical practice relies almost exclusively on self‑reported questionnaires, clinical ratings, and historical information to diagnose and monitor anxiety \cite{Penninx2021Anxiety}.  These tools, while essential, are constrained by subjectivity: cultural nuances, individual insight, and context can all skew scores \cite{evans2013psychologists}.  Importantly, identical anxiety ratings can mask distinct underlying neurobiological circuits, undermining the fidelity of treatment decision‑making \cite{rao2023biotypes}.  This phenomenological heterogeneity presents a substantial barrier to the deployment of targeted neuromodulation therapies such as trans‑cranial direct‑current stimulation (tDCS).

Resting‑state functional magnetic resonance imaging (fMRI) offers a means to objectively probe the large‑scale connectivity patterns that underpin anxiety.  Prior work has successfully identified neuropsychiatric biotypes in younger adults with depression and anxiety \cite{drysdale2017resting,tozzi2024personalized}, yet almost all such studies focus on younger cohorts, leaving a critical gap in our understanding of anxiety in older adults at risk for AD.  The emergence of distinct anxiety biotypes could inform which patients are most likely to benefit from neuromodulation, thereby advancing precision psychiatry in AD.

To bridge this gap, we introduce \textbf{BETA} (\textbf{Biotypes for tDCS Efficacy in Anxiety}), a data‑driven framework that integrates resting‑state fMRI with a transformer‑based variational autoencoder (t‑VAE) and a clinical‑outcome regularization term.  BETA first learns a 50‑dimensional latent representation of functional connectivity that captures the canonical networks implicated in cognitive aging and anxiety—namely, the Cingulo‑Opercular Network (CON), Frontoparietal Control Network (FPCN), Default Mode Network (DMN), medial frontal cortex, cerebellum, motor cortex, and visual systems—using NeuroSynth‑derived masks \cite{Yarkoni2011Neurosynth}.  The resulting latent space is then subjected to deep‑embedded clustering, iteratively refining cluster assignments while enforcing proximity between participants who exhibit similar anxiety change following frontal‑lobe tDCS.  This joint optimization yields five distinct anxiety biotypes.

Our work is the first to identify biotypes for tDCS efficacy for anxiety, and it advances precision neuromodulation in three concise steps:
\begin{enumerate}
    \item \textbf{Novel biotype discovery.}   Five distinct anxiety biotypes are isolated from resting‑state fMRI of older adults at risk for Alzheimer’s disease, each exhibiting a different pattern of response to frontal‑lobe tDCS.
    \item \textbf{Response prediction.}   Two of the biotypes demonstrate marked anxiety relief after tDCS, one shows resistance, and the remaining groups occupy intermediate positions—providing a data‑driven way to anticipate treatment outcomes from imaging alone.
    \item \textbf{Clinical decision support.}   The biotype assignments are mapped to a simple, actionable tDCS recommendation schema, offering a proof–of–concept framework for individualized neuromodulation in geriatric anxiety.
\end{enumerate}

%Figure~\ref{fig:panel} illustrates the BETA pipeline, and Figure~\ref{fig:conn} depicts the hallmark connectivity profiles of each biotype.  Among the five subgroups, one is resistant to frontal‑lobe tDCS, while two demonstrate significant anxiety attenuation after stimulation.  These responsive biotypes are characterized by strengthened connectivity between the lateral occipital cortex (superior division) and medial frontal cortex, as well as between the lateral occipital and intracalcarine cortices \cite{waner2023connecting,takagi2018common}.  By directly linking neurobiological signatures to treatment efficacy, BETA provides a concrete, data‑driven pathway toward individualized tDCS prescriptions for anxiety in older adults at risk for AD.  




% Finally, we have identified five participant groups shown in Fig.\ref{fig2} based on their resting-state fMRI connectivity and network strength and find that they may show differentiate response to treatments like tDCS intervention. %We found that 
% Group 5 may be resistant to frontal-lobe tDCS and Group 1 has a nonsignificant response. Groups 2 and 4 might be recommended frontal-lobe tDCS for anxiety. Individuals whose fMRI is matched to Group 4 would receive strong recommendation for tDCS, % regardless of STAI-S score, 
% whereas Group 2 may receive moderate recommendation for lower STAI-S score and strong recommendation for higher STAI-S score. %The difference between Groups 2 and 4 are significant because 
% Group 4 would be the most likely to benefit from preventative care using tDCS. %Group 2 would still have the potential to benefit from tDCS, but they would be more likely to benefit once anxiety levels have already become clinically presentable. 
% Group 2 would be more likely to benefit from tDCS to mitigate higher anxiety levels. %This information is still important because more effective anxiety interventions is highly desirable. 
% Group 1 could receive tDCS at a lower priority, whereas Group 5 would not be recommended frontal-lobe tDCS. These results are immensely significant because they provide a proof of concept for using fMRI to provide revision to psychiatric guidelines for precision anxiety intervention. 

\section{Methodology}

\begin{figure}[t]%[30]{r}{1\textwidth}
   % \centering
    \includegraphics[width=.75\textwidth, trim=0 0 10cm 0cm]{figures/Figure1.pdf} % Include your figure
    \caption{Method Pipeline.}
    \label{fig1}
\end{figure}

\subsection{Dataset}
This study uses data from a clinical trial on cognitive improvement using tDCS and cognitive training (CT). The participants were healthy older adults 65-89 years of age. Exclusion criteria included neurological disorders, cognitive impairment, opportunistic brain infection, major psychiatric illness, unstable or chronic medical conditions, MRI contraindications, impaired motor response, GABA-ergic medications, or left-handedness. The study was IRB-approved and obtained informed consent. %Data were collected at two sites. Both sites used a 3-T Siemens MRI scanner, but the main site used a 64-channel head coil whereas the secondary site used a 32-channel head coil.
Data were collected at two sites using 3-T Siemens MRI scanners.
Both sites used earplugs for scanner noise and foam padding for head motion. %The structural T1-weighted MRI were acquired with the following parameters: Repetition time (TR) = 1800 ms; Echo Time (TE) = 2.26 ms; flip angle = 8°; voxel size = 1.0 x 1.0 x 1.0 mm3, 176 slices; Field-of-view (FOV) = 256 x 256 mm. 
Resting-state MRI were acquired over 6 minutes with the number of volumes = 120; TR = 3000 ms; TE = 30 ms; flip angle = 70°; voxel size = 3.0 x 3.0 x 3.0 mm3, 44 slices; FOV = 240 x 240 mm. After removing individuals with missing anxiety or fMRI data, the final sample included 199 participants (mean age = 71.27 ± 4.53 years; 61 males and 138 females). The sample comprised 99 Sham tDCS and 100 Active tDCS participants. Descriptive statistics for baseline, post-intervention, and change in State STAI scores for the Sham and Active tDCS groups are summarized in Table~\ref{tab:stai_sham_active}, and the corresponding individual trajectories are visualized in Figure~\ref{fig:stai_spaghetti}.


\begin{table}[ht]
\centering
\small                           % smaller font for a compact look
\setlength{\tabcolsep}{6pt}      % column separation
\renewcommand{\arraystretch}{1.2}% row height
\caption{Sham and Active tDCS State STAI score change from pre-intervention to post-intervention. \textit{n}: number of participants, \textit{STAI-S}: STAI state score, \textit{SD}: Standard deviation, \textit{IQR}: Interquartile range}
\label{tab:stai_sham_active}
\begin{tabular}{
    l   % Measure
    l   % Group
    S[table-format=3.0] % n
    S[table-format=2.2] % Mean
    S[table-format=2.0] % Median
    S[table-format=2.2] % SD
    S[table-format=2.2] % IQR
}
\toprule
\textbf{Measure} & \textbf{Group} & \textbf{n} & \textbf{Mean} & \textbf{Median} & \textbf{SD} & \textbf{IQR} \\
\midrule
Baseline STAI-S  & Sham tDCS   &  99 & 27.33 & 25 & 7.58 &  9.50 \\
Baseline STAI-S  & Active tDCS & 100 & 28.57 & 25 & 8.43 & 14.00 \\
Baseline STAI-S  & All         & 199 & 27.95 & 25 & 8.02 & 12.50 \\
\midrule
Post STAI-S     & Sham tDCS   &  99 & 27.62 & 26 & 8.07 &  9.00 \\
Post STAI-S    & Active tDCS & 100 & 26.68 & 24 & 7.58 & 11.25 \\
Post STAI-S     & All         & 199 & 27.15 & 25 & 7.82 &  9.00 \\
\midrule
STAI-S (Pre-Post)  & Sham tDCS   &  99 &  -0.28 &  0 & 8.64 &  5.50 \\
STAI-S (Pre-Post)   & Active tDCS & 100 & 1.89 &  0 & 7.77 &  7.00 \\
STAI-S (Pre-Post)    & All         & 199 & 0.81 &  0 & 8.27 &  6.00 \\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[ht]
    \centering
    \includegraphics[width=\textwidth]{figures/revised_for_tdcs_status.pdf}
    \caption{Spaghetti plots depicting individual State STAI score changes from baseline to post-intervention for Sham and Active tDCS participants, stratified by baseline anxiety severity.}
    \label{fig:stai_spaghetti}
\end{figure}




\subsection{tDCS intervention}
Participants received twelve weeks of tDCS-paired CT. Conventional tDCS was applied at 2mA intensity using two $5\times7cm^2$ saline-soaked Soterix sponge electrodes at the F3-F4 locations (Dorsolateral Prefrontal Cortex). %Active and sham groups were double blinded and received identical setup. 
Each 40-min CT session began with 20 min of tDCS. The active tDCS was 2mA stimulation for 20 min with 30 s of current ramp up/down. The sham group received 2mA stimulation for 30 s with 30 s of ramp up/down. The sham conditions provided the stimulation sensation without the biologically meaningful effect.

\subsection{Data Preprocessing in the CONN toolbox}
%All preprocessing took place within the functional connectivity toolbox, CONN, using MATLAB 2022b. 
%The default CONN preprocessing was performed, including 
The preprocessing included functional realignment, unwarping, slice timing correction, segmentation, co-registration, normalization to MNI space, and smoothing with an 8-mm Gaussian filter (https://web.conn-toolbox.org/fmri-methods/preprocessing-pipeline). One participant was removed for head motion. We used NeuroSynth \cite{Yarkoni2011Neurosynth} to extract ROIs related to congitive aging and anxiety, then we inputted these ROIs into CONN to extract the functional connectivity measures.

%\subsection{Functional Connectivity Measures}
%This paper selected brain regions-of-interest (ROIs) %for analysis that were inspired by previous research on
%that have been indicated in cognitive aging and anxiety \cite{waner2023connecting,takagi2018common}. %The primary areas of interest are brain regions 
%These ROIs fall within the Cingulo-Opercular Network (CON), Frontopartietal Control Network (FPCN), Default Mode Network (DMN), the Medial Frontal Cortex, the Cerebellum, the Motor Cortex, the Visual Cortex, and the Visual Association area. %The functional connectivity networks that are related to these regions was extracted using the platform NeuroSynth, which is an open-access tool to derive fMRI regions that are connected to specific search terms or tasks across studies. 
%Here, we use NeuroSynth \cite{Yarkoni2011Neurosynth} to  extract the relevant networks.

%NeuroSynth is an open-access tool to derive fMRI activations that relate to specific search parameter across studies.
%This approach ensures that the brain analysis in this study is based on reliable imaging measures across participant trials. 

\subsection{The Deep Embedded Clustering Pipeline}
%\subsubsection{Algorithm}
%The pipeline inputs the functional connectivity network matrices into an initial feature extraction phase so that it can derive the most important features that are necessary to differentiate biotype groups. 
VAEs have shown success in extracting informative fMRI features \cite{kim2021representation,han2019variational,qiang2021modeling}. Hence, the feature dimentionality reduction phase applies a transformer VAE (t-VAE) to extract important features from the functional connectivity networks. We construct our t-VAE with a transformer encoder and decoder\cite{waswani2017attention}. % so that the VAE can harness the power of transformers. 
% across the input data Transformers use attention mechanisms to capture long-range relationships 
  The learned features of 50-dimension latent representation as output of transformer encoder  are clustered with k-means for the initial estimation. Then a finetuning stage iterates over the clusters while optimizing the KL Divergence (KL) \cite{kullback1951information} and Mean Squared Error (MSE) \cite{gauss_method_of_least_squares} losses. The latent space had a dimensionality of 50, which was decided empirically based on loss convergence. %Figure \ref{fig1} displays the entire pipeline.

%\subsubsection{Regularization} The t-VAE will learn a latent space that is more indicative of the entire sequence of the functional connectivity.

 %that employs clinical measures to help guide the constructed latent space during feature extraction. This term allows for the option to combine imaging and clinical data in one model. %At the same time, the term is provided within an optional loss term so that the code still function if the user only has fMRI input data.% and still wants to perform anxiety analysis.
\noindent We also design an efficacy regularization term in Equation \ref{eq1} based on State-Trait Anxiety Inventory (STAI) score that assesses anxiety \cite{spielberger1999measuring}. The STAI assessment consists of 40 self-entered questions where 20 relate to "state" (i.e., STAI-S) anxiety, temporary anxiety in response to stressors, and 20 relate to "trait" anxiety (i.e., STAI-T), a long-term tendency to experience anxiety across different situations. The State-Trait Anxiety Inventory (STAI) has two subscales, each with a score range of 20 to 80. We focused on the STAI-S score due to the short-term intervention window (12 weeks). Individuals with an STAI-S score above 38 are generally regarded as having moderate/severe state anxiety symptoms and having clinically relevant anxiety levels (\textbf{CRAL}).


% \indent Equation \ref{eq1} shows the clinical term for regularization.

\begin{equation}
%x = (STAI_{Pre} - STAI_{Post}) / STAI_{Pre}
x = STAI_{Post} / STAI_{Pre}
\label{eq1}
\end{equation}

\noindent where $x$ is the clinicial comparison term, %fractional change in the STAI Score, 
$STAI_{Pre}$ and $STAI_{Post}$ is the STAI score before and after intervention.

The overall clinical loss term is as presented in Equation \ref{eq2}. 
The term on the left of the multiplication represents the similarity term that is derived from the clinical term in \ref{eq1}. We base our similarity term on kernel similarity due to its long history of success in machine learning challenges \cite{hofmann2008kernel}. The similarity term is multiplied by the latent distances of the pair-wise terms within a given training batch. The similarity term is bigger when two participants have similar clinical scores and goes towards 0 when two participants have very different clinical scores. The second term in the overall loss equation represents the closeness of two participants in the latent space. The effect of the multiplication is that the model is highly penalized for placing two participants with very similar clinical scores to be very far in the latent space. 
\begin{equation}
Loss_{Clinical} = torch.exp(- |x_1 - x_2|^2 \div 2 \sigma ^ 2) * torch.cdist(z_1, z_2)^2
\label{eq2}
\end{equation}
where $x_1$ and $x_2$ are derived using using Equation \ref{eq1}. The $x_1 - x_2$ component ends up being pair-wise across each individual pair of participant data in a given training batch. $\sigma$ is a scalar constant, $z_1$ and $z_2$ are data representations in the VAE latent space, and $torch.cdist(z_1,z_2)^2$ represents the pair-wise distance between two participants' data in the transformer VAE's latent space.
\indent Equation \ref{eq4} represents the total loss for training the t-VAE.
\begin{equation}
Loss_{Total} = Loss_{MSE} + KL divergence + \beta * Loss_{Clinical}
\label{eq4}
\end{equation}
\noindent where $Loss_{Total}$ is the total training loss, $Loss_{MSE}$ is the mean-squared error loss, KL divergence is also used, $\beta$ is the weighting of the clinical term's contribution to the loss, and $Loss_{Clinical}$ is our proposed loss from Equation \ref{eq2}.  

%\indent Equation \ref{eq2} represents the similarity term that is derived from the clinical term in \ref{eq1}. We base our similarity term on kernel similarity due to its long history of success in machine learning challenges \cite{hofmann2008kernel}.

%\begin{equation}
%Similarity = torch.exp(- |x_1 - x_2|^2 \div 2 \sigma ^ 2)
%\label{eq2}
%\end{equation}
%where $x_1$ and $x_2$ are derived using using \ref{eq1}. The $x_1 - x_2$ component ends up being pair-wise across each individual pair of participant data in a given training batch. $\sigma$ is a scalar constant. 

%\indent The final clinical loss term is as presented in Equation \ref{eq3}. The similarity term is multiplied by the latent distances of the pair-wise terms within a given training batch. The similarity term is bigger when two participants have similar clinical scores and goes towards 0 when two participants have very different clinical scores. The second term in the overall loss equation represents the closeness of two participants in the latent space. The effect of the multiplication is that the model is highly penalized for placing two participants with very similar clinical scores to be very far in the latent space. 

%\begin{equation}
%Loss_{Clinical} = Similarity * torch.cdist(z_1, z_2)^2
%\label{eq3}
%\end{equation}
%Where Similarity is the pair-wise term that is derived from Equation \ref{eq1}, $z_1$ and $z_2$ are participant representations in the latent space of the transformer VAE, and $torch.cdist(z_1,z_2)^2$ represents the pair-wise distance between two participants' data in the transformer VAE's latent space.


\subsection{Inference} %When we have a new fMRI data point, we can directly pass it through the 
%Given a fMRI image, we can pass it through the trained pipeline without requiring the STAI score changes to tDCS intervention. The result will be the fMRI's biotype that represents the unique fMRI network activations and its likely response to tDCS.

Participants can be classified into Anxiety tDCS Efficacy Biotypes (AtEB) at inference time with only their fMRI images as input. At inference, users would first pass the unseen participant’s fMRI through a pipeline of CONN fMRI preprocessing, ROI selection, and  trained t-VAE to perform dimensionality reduction. The output is the 50 AtEB features from the t-VAE’s latent space projection. Then, the pipeline computes the mean  Euclidean distance from the new fMRI’s 50-feature representation to the representation of each cluster group’s centroid. The new fMRI is classified as belonging to the cluster group with the minimum distance. The STAI-s score is only used during training to determine the 50 optimal AtEB features using the t-VAE. It is no longer required during inference since these features were already determined. This is important because it means that we can predict a participant’s response to intervention before they have actually had tDCS. 

\subsection{Group-level differences in Functional Connectivity}
%This experiment extracted the functional connectivity networks that led to the differences in anxiety presentations across the clusters. 
We used one-sample t-testing to extract the functional connectivity differences that are significant for each cluster compared to the entire population. %The pipeline used many one-sample t-tests for each cluster and functional connectivity network, so 
The false discovery rate (FDR) method \cite{benjamini1995controlling} was applied to correct for multiple comparisons.

\subsection{tDCS Efficacy within Clusters} %Group-level differences in tDCS Outcome}
%need to edit, wrote a little lot based on when we talked on the other day
Statistical tests were performed on each cluster to determine if tDCS could be promising for that particular anxiety subtype. 
%Statistical tests within each cluster determined if there was a significant difference in intervention outcome between tDCS and sham tDCS. The cluster-wise analysis is to predict if tDCS could be a promising intervention for each particular anxiety subtype. %We believe that assessing whether tDCS could help a particular anxiety biotype is more directly relevant than determining which groups respond better to tDCS, since in practice anyone who benefits from it would be encouraged to receive it. 
Thus, this analysis isolated each biotype cluster and performed a t-test to assess for significant difference in active vs. sham tDCS. %Specifically, the t-test assessed the hypothesis ``do the participants who received active tDCS have greater improvement in anxiety than the participants who received sham tDCS?'' 
%Cluster three was excluded because it ended up only containing participants who received active tDCS. 
The analysis was carried out separately depending on a participant's initial STAI-S score. Three tests were performed: all participants, high anxiety (STAI-S>38), and mild/moderate anxiety (STAI-S<38).

\section{Experiments and Results}

\subsection{Overview of Data-Driven Clusters}
We identified five clusters reflecting distinct demographic and psychological profiles (Table \ref{tb1}). Based on prior work showing strong depression–anxiety comorbidity \cite{kessler2008co,lamers2011comorbidity}, we include BDI-II subscales to capture shared affective variance that may influence connectivity patterns.

Two broad trends emerged.
First, Clusters 1 and 2 contain higher proportions of female participants and exhibit elevated STAI and BDI-II scores, suggesting more symptom-expressive subgroups.
Second, Clusters 4 and 5 represent the largest portions of the sample with moderate symptoms and balanced sex distributions, whereas Cluster 3 forms a small outlier group with notably low anxiety and depression levels.

These behavioral distinctions provide important context for interpreting the functional connectivity differences described next.



\begin{table}[ht]
\centering
\small
\setlength{\tabcolsep}{8pt}
\renewcommand{\arraystretch}{1.25}
\caption{
Demographic, clinical, and connectivity characteristics across the five data-driven clusters.
\textit{n} = number of participants; \textit{SD} = standard deviation.
BDI-II subscales include: Somatic (physical/vegetative symptoms), 
Affective (emotional symptoms), and Cognitive (self-critical or distorted thinking patterns).
}
\label{tab:cluster_demo_full}
\begin{tabular}{lccccc}
\toprule
\textbf{Measure} & \textbf{C1} & \textbf{C2} & \textbf{C3} & \textbf{C4} & \textbf{C5} \\
\midrule
n & 29 & 39 & 3 & 68 & 60 \\
Proportion (\%) & 14.57 & 19.60 & 1.51 & 34.17 & 30.15 \\
Age (Mean) & 72.62 & 70.49 & 71.33 & 70.24 & 72.30 \\
Age (SD) & 5.43 & 3.46 & 0.58 & 4.25 & 4.81 \\
STAI-State Pre (Mean) & 28.55 & 29.15 & 23.00 & 26.94 & 28.28 \\
STAI-State Pre (SD) & 8.20 & 8.87 & 2.65 & 6.98 & 8.62 \\
STAI-Trait (Mean) & 29.07 & 28.56 & 24.00 & 27.91 & 28.37 \\
STAI-Trait (SD) & 7.21 & 8.41 & 3.00 & 6.28 & 7.57 \\
BDI Total (Mean) & 4.72 & 2.59 & 2.33 & 2.76 & 3.65 \\
BDI Total (SD) & 4.17 & 3.23 & 2.08 & 3.76 & 4.08 \\
BDI Somatic (Mean) & 3.10 & 1.64 & 1.67 & 1.78 & 2.38 \\
BDI Somatic (SD) & 2.64 & 1.90 & 1.53 & 2.15 & 2.54 \\
BDI Affective (Mean) & 0.79 & 0.38 & 0.00 & 0.44 & 0.55 \\
BDI Affective (SD) & 1.01 & 0.75 & 0.00 & 0.84 & 0.98 \\
BDI Cognitive (Mean) & 0.83 & 0.56 & 0.67 & 0.54 & 0.72 \\
BDI Cognitive (SD) & 1.26 & 1.21 & 0.58 & 1.51 & 1.40 \\
Male (n) & 6 & 10 & 0 & 23 & 22 \\
Female (n) & 23 & 29 & 3 & 45 & 38 \\
Male (\%) & 20.69 & 25.64 & 0.00 & 33.82 & 36.67 \\
Female (\%) & 79.31 & 74.36 & 100.00 & 66.18 & 63.33 \\
Mean Red-Box Connectivity & 0.2365 & 0.3241 & 0.0886 & 0.2889 & 0.2065 \\
\bottomrule
\end{tabular}
\end{table}



\subsection{Group-level differences in Functional Connectivity}
Figure \ref{fig2} displays the five connections that differed most strongly between each cluster and the population average. Blue edges denote weaker-than-average connectivity, whereas red edges indicate stronger coupling. These quantitative differences are summarized in Table \ref{tb1}. Together, these results highlight that clusters differ not only in symptom severity but also in large-scale network organization, particularly within default mode (DMN), fronto-parietal control, salience, and visual–frontal pathways.

\subsection{Cluster-Specific Functional Connectivity Profiles}
Below we describe each cluster’s connectivity profile and its potential functional implications.
 % reflects how each connection in this figure differs .
\subsubsection{Cluster 1 – Predominantly hypoconnected fronto-parietal/DMN subtype}


Cluster 1 was characterized by widespread reductions in connectivity, with most weakened edges occurring between fronto-parietal control regions (middle and superior frontal gyri, inferior frontal gyrus) and default mode network (DMN) hubs in the angular gyrus. Additional reductions appeared in visual–frontal pathways linking the lateral occipital cortex to medial and lateral prefrontal regions. Only a small subset of cerebellar–frontal edges remained preserved or slightly increased. Overall, Cluster 1 reflected a globally hypoconnected profile, particularly affecting long-range control–DMN integration. 

\subsubsection{Cluster 2 – Hyperconnected fronto-parietal–DMN subtype (tDCS-responsive)}


Cluster 2 showed robustly stronger connectivity across multiple systems, including enhanced coupling between DMN parietal hubs and frontal control regions, insula/opercular areas and prefrontal cortex, and visual cortex with medial and lateral prefrontal cortex. This cluster exhibited one of the most integrated network profiles, with consistent strengthening of fronto-parietal, DMN, salience, and visual–frontal pathways. Given the broad pattern of increased large-scale integration, Cluster 2 represented a strongly hyperconnected subtype, consistent with the group showing clinically meaningful improvement following tDCS.

\subsubsection{Cluster 3 – Mixed profile with weakenings in DMN–control pathways and selective cerebellar/temporal strengthening}


Cluster 3 demonstrated a mixed pattern, with weaker connectivity in most DMN–control and visual–frontal edges, similar to Cluster 1, but stronger connectivity in a small set of edges linking cerebellar regions, the temporal pole, and medial prefrontal cortex. This yielded a profile in which canonical control–DMN pathways were reduced, while limbic–cerebellar circuits were relatively enhanced. Overall, Cluster 3 represented a hybrid subtype with selective strengthening of cerebellar and temporal networks against a background of broader hypoconnectivity in control-related pathways.

\subsubsection{Cluster 4 – Strongly hyperconnected regulatory subtype (broadly tDCS-responsive)}


Cluster 4 exhibited one of the most consistently elevated connectivity profiles, with strengthened edges across DMN–fronto-parietal, salience–prefrontal, and insula/opercular–frontal networks. Nearly every major pathway showed preserved or increased coupling, and no major edges were significantly reduced. This produced a high-connectivity, highly integrated subtype, in which long-range communication across control, DMN, and salience systems was particularly strong. Notably, this cluster showed anxiety improvement in all participants who received tDCS, aligning with its cohesive and well-integrated connectivity structure.

\subsubsection{Cluster 5 – Globally hypoconnected subtype across control, DMN, visual, and salience systems}


Cluster 5 showed the most widespread reductions in connectivity, with nearly all edges significantly weaker than the full sample. Reduced coupling was prominent in control–DMN pathways, insula/opercular–frontal circuits, and visual–prefrontal connections. This resulted in a globally segregated network organization, marked by diminished communication across systems typically involved in executive regulation, self-referential processing, multisensory integration, and perceptual–cognitive coupling. Cluster 5 therefore captured a broadly low-connectivity subtype, contrasting sharply with the hyperconnected patterns observed in Clusters 2 and 4.


\begin{figure}[t]%[30]{r}{1\textwidth}
    %\centering
    \includegraphics[width=\textwidth]{figures/Figure2.pdf} % Include your figure
    \caption{Distinct hyper- and hypo-connectivity in fMRI from the five subtypes.}
    \label{fig2}
\end{figure}

\subsection{tDCS Efficacy within Clusters} %Group-level differences in tDCS Outcome}
%The clustering pipeline did not use tDCS membership as part of the clustering requirement.%, since that would not factor in the initial anxiety biotype. 
%Hence, it was possible for groups to be unevenly distributed across active and sham tDCS. Cluster 3 ended up only containing participants who received active tDCS and was therefore ineligible for this comparison. 
% We report the tDCS efficacy within each cluster in  Tab.\ref{tb2}. High anxiety subjects of Cluster 2, 4 and 5 (C2,4,5) have  shown significant improvements in tDCS. Anxiety improvement  is most significant in C2, showing the necessity of tDCS treatment. Subjects of C4 also benefited from treatment. Very interestingly, for C5 patients, even knowing being treated can result in even slightly better improvement. ON the contrary, C1 subjects received minor improvement in anxiety for high anxiety and even negative effect for mild/moderate anxiety patients, implying that alternative treatment. Another important finding is that C3 patients became even worse, no matter receiving actual or placebo treatment. Further exploring the mechanism would contribute to precision indication for applying tDCS. 


The cluster-specific connectivity patterns provide a useful framework for interpreting heterogeneity in tDCS response (Table \ref{tb2}). Three clusters—C2, C4, and C5—show clear anxiety reductions among high-anxiety participants receiving active stimulation. Notably: (1) C2 exhibits the strongest clinical improvement, consistent with its highly integrated fronto-parietal–DMN architecture. (2) C4 shows robust benefit across all tDCS recipients, aligning with its globally hyperconnected profile. (3) C5 improves even under sham stimulation, suggesting strong expectancy effects in this hypoconnected group.

In contrast, C1 subjects show only minimal improvement among high-anxiety individuals and even slight worsening among mild/moderate cases, indicating that alternative interventions may be more appropriate. A particularly notable finding is that C3 patients worsen regardless of receiving active or sham stimulation. Further investigation into the mechanisms underlying C3’s deterioration could provide important insights for precision indications and safe application of tDCS.



% fractional improvement in anxiety. Among all 5 clusters, subjects of Cluster 4 (C4) has received most significant anxiety improvement with tDCS treatment under all levels of anxiety. %, including in a separate test for only those with mild/moderate anxiety. 
% Subjects of cluster 2 (C2) also showed improvement from tDCS, but this was only significant in subjects with clinically significant (high) anxiety.  On the contrast, patients of clusters 1 and 5 did not show significant difference between active and sham (control) tDCS under either high or mild/moderate anxiety.  However tDCS did appear to have a positive effect on Cluster 1. Cluster 3 was excluded due to an uneven data distribution of sham tDCS. %one that only examined participants whose initial anxiety level was deemed clinical significant (STAI-S score above 38). During the first test, cluster 4 showed that active tDCS was significantly correlated with anxiety improvement in all participants. Cluster 2 also showed improvement in anxiety with active tDCS, but this improvement was only significant in experiment two that was limited to clinically significant anxiety. Clusters 1 and 5 did not show significant difference between active and sham tDCS in any participant subgroup. 

%Generally, improvement with active tDCS was seen in clusters with greater connectivity between the lateral occipital cortex and the frontal medial cortex, the temporal pole and the Cerebellum, the lateral occipital cortex and the intracalcine cortex, the superior frontal gyrus and the angular gyrus, the lateral occipital cortex and the frontal operculum cortex, the angular gyrus and the precentral gyrus, the midfrontal gyrus and the angular gyrus, the inferior frontal gyrus and the cerebellum, the angular gyrus and the lateral occipital cortex, the insular cortex and the inferior frontal gyrus, the superior frontal gyrus and the inferior frontal gyrus, and the midfrontal gyrus and the parietal operculum cortex

Generally, significant improvement with active tDCS was observed in clusters with greater connectivity of the lateral occipital cortex, angular gyrus, inferior and superior frontal gyri, and midfrontal gyrus. Some important connections included the cerebellum, insular cortex, temporal pole, and precentral gyrus.

\begin{table}[!t]
\centering
\caption{Average Functional Connectivity Strengths. C1-C5 = Clusters 1-5. All brain region acronyms follow the table of definitions from Figure \ref{fig2}. $\uparrow$ and $\downarrow$ mean significantly stronger and significantly weaker connections for the given cluster, respectively. The impact of tDCS: * = clinically significant anxiety improved with tDCS, ** = all anxiety improved with tDCS, $\times$ = cluster only had active tDCS.} %Brain Regions: $sLOC_{L}$ = Lateral Occipital Cortex superior division left, MedFC = Frontal Medial Cortex, $ICC_{L}$ = Intracalcarine Cortex left, $SFG_{L}$ = Superior Frontal Gyrus left, $AG_{R}$ = Angular Gyrus right, $FO_{R}$ = Frontal Operculum Cortex right, $PreCG_{R}$ = Precentral Gyrus right, $IFG_{tri_{R}}$ = Inferior Frontal Gyrus pars triangularis right, Ver8 = Vermis 8, $MidFG_{L}$ = Middle Frontal Gyrus left, $AG_{L}$ = Angular Gyrus left, $sLOC_{R}$ = Lateral Occiptal Cortex superior division right, $TP_{R}$ = Temporal Pole right, $IFG_{per_{R}}$ = Inferior Frontal Gyrus pars opercularis left, $Cereb8_{R}$ = Cerebellum 8 right, $MidFG_{R}$ = Middle Frontal Gyrus right, $pSMG_{R}$ = Supramarginal Gyrus posterior division right, $PO_{R}$ = Parietal Operculum Cortex right, $IC_{L}$ = Insular Cortex left, $SFG_{R}$ = Superior Frontal Gyrus right}
%\renewcommand{\arraystretch}{1.5} 
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf{Group} & \textbf{C1} & \textbf{C2$^*$} & \textbf{C3$^\times$} & \textbf{C4$^{**}$} & \textbf{C5} & \textbf{All Participants} \\
\hline
% 75-80
%\textbf{sLOC l - MedFC} & 0.093001 & 0.399031 & 0.262934 & 0.218160 & 0.161316 & 0.218904 \\
\textbf{$sLOC_{L} - MedFC$} & $0.0930 \downarrow$ & $0.399 \uparrow$ & $0.263 \uparrow$ & 0.218 & $0.161 \downarrow$ & $0.219\pm0.0380$ \\
\hline
% 161 - 38
%\textbf{Ver8 - MidFG r} & 0.196926 & -0.013562 & -0.032407 & 0.022012 & -0.038060 & 0.021598 \\
\textbf{$Ver8 - MidFG_{R}$} & $0.197 \uparrow$ & $-0.0136 \downarrow$ & $-0.0324 \downarrow$ & 0.0220 & $-0.0381 \downarrow$ & $0.0216\pm0.0290$ \\
\hline
% 40 - 161
%\textbf{IFG tri r - Ver8} & 0.197325 & 0.032275 & -0.274465 & 0.011056 & -0.015269 & 0.030118 \\
\textbf{$IFG_{tri_{R}} - Ver8$} & $0.197 \uparrow$ & 0.0332 & $-0.274 \downarrow$ & 0.0111 & $-0.0153 \downarrow$ & $0.0301\pm0.0310$ \\
\hline
% 46 - 151
%\textbf{TP r - Cereb8 r} & -0.067199 & 0.171204 & 0.249726 & 0.088536 & 0.067944 & 0.078263 \\
\textbf{$TP_{R} - Cereb8_{R}$} & $-0.0672 \downarrow$ & $0.171 \uparrow$ & $0.250 \uparrow$ & 0.0885 & 0.0679 & $0.0783\pm0.0310$ \\
\hline
% 75-79
%\textbf{sLOC l - ICC l} & 0.118248 & 0.370625 & 0.154245 & 0.205916 & 0.181326 & 0.217227 \\
\textbf{$sLOC_{L} - ICC_{L}$} & $0.118 \downarrow$ & $0.371 \uparrow$ & $0.154 \downarrow$ & 0.206 & $0.181 \downarrow$ & $0.217\pm0.0350$ \\
\hline
% 37-72
%\textbf{SFG l - AG r} & 0.403543 & 0.460777 & -0.000403 & 0.449873 & 0.316921 & 0.398384 \\
\textbf{$SFG_{L} - AG_{R}$} & 0.404 & $0.461 \uparrow$ & $-0.000403 \downarrow$ & $0.450 \uparrow$ & $0.317 \downarrow$ & $0.398\pm0.0340$ \\
\hline
% 75-107
%\textbf{sLOC l - FO r} & 0.053334 & 0.264016 & -0.128329 & 0.154871 & 0.127491 & 0.148940 \\
\textbf{$sLOC_{L} - FO_{R}$} & $0.0533 \downarrow$ & $0.264 \uparrow$ & $-0.128 \downarrow$ & 0.155 & 0.127 & $0.149\pm0.0320$ \\
\hline
% 72-44
%\textbf{AG r - PreCG r} & 0.323187 & 0.406659 & 0.005909 & 0.361005 & 0.246395 & 0.324532 \\
\textbf{$AG_{R} - PreCG_{R}$} & 0.323 & $0.407 \uparrow$ & $0.00591 \downarrow$ & $0.361 \uparrow$ & $0.246 \downarrow$ & $0.325\pm0.0320$ \\
\hline
% 39 - 72
%\textbf{MidFG l - AG r} & 0.253904 & 0.363979 & -0.065182 & 0.383576 & 0.230479 & 0.307914 \\
\textbf{$MidFG_{L} - AG_{R}$} & $0.254 \downarrow$ & $0.364 \uparrow$ & $-0.0652 \downarrow$ & $0.384 \uparrow$ & $0.230 \downarrow$ & $0.308\pm0.0360$ \\
\hline
% 43-151
%\textbf{IFG oper l - Cereb 8 r} & -0.022498 & 0.214157 & 0.263021 & 0.097536 & 0.062800 & 0.094921 \\
\textbf{$IFG_{oper_{L}} - Cereb8_{R}$} & $-0.0225 \downarrow$ & $0.214 \uparrow$ & $0.263 \uparrow$ & 0.0975 & 0.0628 & $0.0949\pm0.0330$ \\
\hline
% 73-74
%\textbf{AG l - sLOC r} & 0.476736 & 0.635468 & 0.283528 & 0.661314 & 0.515208 & 0.579603 \\
\textbf{$AG_{L} - sLOC_{R}$} & $0.477 \downarrow$ & $0.635 \uparrow$ & $0.284 \downarrow$ & $0.661 \uparrow$ & $0.515 \downarrow$ & $0.580\pm0.0370$ \\
\hline
% 35-43
%\textbf{IC l - IFG oper l} & 0.499436 & 0.557480 & 0.070352 & 0.573667 & 0.445260 & 0.513374 \\
\textbf{$IC_{L} - IFG_{oper_{L}}$} & 0.499 & $0.557 \uparrow$ & $0.0704 \downarrow$ & $0.574 \uparrow$ & $0.445 \downarrow$ & $0.513\pm0.0350$ \\
\hline
% 36 - 43
%\textbf{SFG r - IFG oper l} & 0.530492 & 0.654696 & 0.169204 & 0.668212 & 0.517134 & 0.592420 \\
\textbf{$SFG_{R} - IFG_{oper_{L}}$} & $0.530 \downarrow$ & $0.655 \uparrow$ & $0.169 \downarrow$ & $0.668 \uparrow$ & $0.517 \downarrow$ & $0.592\pm0.0400$ \\
\hline
% 36-40
%\textbf{SFG r - IFG tri r} & 0.639737 & 0.713868 & 0.111889 & 0.728628 & 0.645093 & 0.678297 \\
\textbf{$SFG_{R} - IFG_{tri_{R}}$} & $0.640 \downarrow$ & 0.714 & $0.112 \downarrow$ & $0.729 \uparrow$ & 0.645 & $0.678\pm0.0380$ \\
\hline
% 161 - 70
%\textbf{Ver 8 - pSMG r} & 0.190720 & 0.024320 & 0.055713 & 0.031909 & 0.003407 & 0.045330 \\
\textbf{$Ver8 - pSMG_{R}$} & $0.191 \uparrow$ & 0.0243 & 0.0557 & 0.0319 & $0.00341 \downarrow$ & $0.0453\pm0.0280$ \\
\hline
% 38 - 111
%\textbf{MidFG r - PO r} & -0.102735 & -0.070167 & 0.290456 & -0.034033 & -0.159908 & -0.084187 \\
\textbf{$MidFG_{R} - PO_{R}$} & -0.103 & -0.0702 & $0.290 \uparrow$ & $-0.0340 \uparrow$ & $-0.160 \downarrow$ & $-0.0842\pm0.0320$ \\
\hline
\end{tabular}
}
\label{tb1}
\end{table}


\begin{table}[!t]
\centering
\caption{Change in STAI score (Pre–Post) by cluster and condition. Higher values indicate greater symptom improvement. Values reported as mean change with sample size (n).}
\small
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.35}
\begin{tabular}{lccccc}
\toprule
\textbf{Group} & \textbf{C1} & \textbf{C2} & \textbf{C3} & \textbf{C4} & \textbf{C5} \\
\midrule

\makecell[l]{\textbf{Mild/Minimal}\\Sham tDCS} 
    & \makecell{-0.128\\(n = 12)}
    & \makecell{0.0129\\(n = 14)}
    & \makecell{-0.268\\(n = 2)}
    & \makecell{-0.112\\(n = 29)}
    & \makecell{0.0308\\(n = 32)} \\

\makecell[l]{\textbf{Mild/Minimal}\\Active tDCS} 
    & \makecell{-0.0473\\(n = 12)}
    & \makecell{0.0603\\(n = 20)}
    & \makecell{-0.200\\(n = 1)}
    & \makecell{0.0606\\(n = 34)}
    & \makecell{0.0120\\(n = 18)} \\

\makecell[l]{\textbf{Moderate/Severe}\\Sham tDCS} 
    & \makecell{0.0570\\(n = 4)}
    & \makecell{0.314\\(n = 2)}
    & \makecell{N/A\\(n = 0)}
    & \makecell{N/A\\(n = 0)}
    & \makecell{0.284\\(n = 4)} \\

\makecell[l]{\textbf{Moderate/Severe}\\Active tDCS} 
    & \makecell{0.0769\\(n = 1)}
    & \makecell{0.556\\(n = 3)}
    & \makecell{N/A\\(n = 0)}
    & \makecell{0.156\\(n = 5)}
    & \makecell{0.172\\(n = 6)} \\

\bottomrule
\end{tabular}
\label{tb2}
\end{table}



\begin{table}[!t]
\centering
\caption{
Change in STAI-State score (Pre--Post, in points) by baseline anxiety group, stimulation condition, and cluster.
Positive values indicate symptom reduction (higher pre- than post-intervention scores).
Values are Mean (SD), with sample size shown as $n$.
For each cluster, change scores were tested against zero using one-sample \textit{t}-tests when Shapiro--Wilk normality was not violated (p~{>}~0.05) and Wilcoxon signed-rank tests otherwise (Low STAI sham clusters 1, 4, and 5).
Clusters with $n \le 1$ or $n = 0$ were not formally tested.
Cells with p~{<}~0.05 (uncorrected) are marked with an asterisk (*).
}
\small
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{lccccc}
\toprule
\textbf{Group} & \textbf{C1} & \textbf{C2} & \textbf{C3} & \textbf{C4} & \textbf{C5} \\
\midrule
\makecell[l]{Minimal/Mild\\Sham} & \makecell{-4.0 (8.47)\\$n = 12$} & \makecell{-0.5 (6.50)\\$n = 14$} & \makecell{-6.5 (3.54)\\$n = 2$} & \makecell{-2.7 (7.43)\\$n = 29$} & \makecell{0.9 (7.99)\\$n = 32$} \\
\makecell[l]{Minimal/Mild\\Active} & \makecell{-1.2 (6.58)\\$n = 12$} & \makecell{0.1 (5.56)\\$n = 20$} & \makecell{-4.0 (N/A)\\$n = 1$} & \makecell{1.8 (5.82)\\$n = 34$} & \makecell{-0.9 (5.73)\\$n = 18$} \\
\makecell[l]{Moderate/Severe\\Sham} & \makecell{2.5 (9.57)\\$n = 4$} & \makecell{15.0 (2.83)\\$n = 2$} & \makecell{N/A\\$n = 0$} & \makecell{N/A\\$n = 0$} & \makecell{12.8 (13.00)\\$n = 4$} \\
\makecell[l]{Moderate/Severe\\Active} & \makecell{3.0 (N/A)\\$n = 1$} & \makecell{26.0 (6.08)*\\$n = 3$} & \makecell{N/A\\$n = 0$} & \makecell{7.0 (11.47)\\$n = 5$} & \makecell{7.3 (6.95)*\\$n = 6$} \\
\bottomrule
\end{tabular}
\label{tb2}
\end{table}




\subsection{Ablation Analysis}

To evaluate the contribution of clinical information to cluster formation and treatment prediction, we compared models trained with and without the clinical loss term $L_{Clinical}$. Only the full model reproduced significant cluster-wise differences in anxiety change (Welch’s ANOVA: 0.023 vs. 0.102 for the ablation model) and correctly identified clusters with meaningful tDCS effects. This demonstrates that incorporating clinical information helps the latent space capture intervention-relevant variations that purely neuroimaging-based representations miss.

\section{Discussions}
This work derived fMRI-derived biomarkers of anxiety within older adults. Our findings indicate regions like the lateral occipital cortex (LOC) and the angular gyrus (AG) in anxiety. Previous research has supported that anxiety is often characterized by impaired functioning between regions for sensory processing (e.g., LOC,AG) and higher cognitive control (e.g. gyri in prefrontal regions) \cite{langhammer2024resting}. The LOC is associated with visual processing \cite{li2020spontaneous}. Most anxiety work more commonly relates hyperactivity in the LOC with anxiety \cite{langhammer2024resting}. The particular connection that occurred the most often between clusters was the LOC's connection to the Frontal Medial Cortex. The mechanism may possibly be via heightened fear processing and response \cite{li2020spontaneous}. However, our results indicate that there may be differential anxiety biotypes that correspond to reduced activity of the lateral occipital cortex. The reduced activity may possibly contribute to anxiety through a reduced ability to process environmental stimuli \cite{van2021effects}. The current study indicates that increased activity of the LOC is more associated with improvement in anxiety with tDCS. It seems like tDCS likely improves the ability of this region to exhibit inhibitory control. There may be a greater need to study how reduced activity in this region can be linked to anxiety so that interventions can be improved for these individuals. Another main area of interest is the AG, particularly its connection to the medial frontal gyrus. This connection has potential impacts on attention, memory, and language skills \cite{de2020causal}. TDCS appears to be more likely to improve individuals who have hyperactivity in this region compared to hypoactivity. Previous research has focused on the AG as a target for tDCS to improve cognitive abilities related to dementia and memory \cite{Hu2022Efficacy}. Our observations in the current study indicate that tDCS may improve cognitive performance related to the angular gyrus by helping regulate attention.

This study was limited by the dataset size. The original data source was based on relatively healthy older adults at risk of AD; it was not an anxiety dataset. Some clustering groups did not include enough data to compare individuals who had clinically significant anxiety across active and sham tDCS. For example, group 3 did not include clinically relevant anxiety, and group 4 did not include clinically relevant anxiety with sham tDCS. On average, each group excluding group 3 was composed of about 10-20 percent clinically relevant anxiety.

\section{Conclusions}

Our results identify participant groups who may differentially respond to tDCS using their resting-state fMRI connectivity. This pipeline can process new fMRI data to give an intervention recommendation for an individual passed on their fMRI scan and initial anxiety score. Our future work will expand to a wider range of participant characteristics (e.g., different hospitals, comorbities, ages) and intervention types to further expand our use cases. The current work is an important step in the field of precision psychiatry and personalized interventions. %These methods can be applied to a wider range of interventions in the future to further expand our clinical recommendations.

%\section{Conclusion}
%We identify participant groups based on their resting-state fMRI connectivity and network strength who may differentially respond to tDCS intervention. %We found that 
%Group 5 may be resistant to frontal-lobe tDCS, Group 1 has a nonsignificant response, and Groups 2 and 4 have evidence to recommend frontal-lobe tDCS for anxiety. Individuals whose fMRI is matched to Group 4 would receive strong recommendation for tDCS, % regardless of STAI-S score, 
%whereas Group 2 may receive moderate recommendation for lower STAI-S score and strong recommendation for higher STAI-S score. %The difference between Groups 2 and 4 are significant because 
%Group 4 would be the most likely to benefit from preventative care using tDCS. %Group 2 would still have the potential to benefit from tDCS, but they would be more likely to benefit once anxiety levels have already become clinically presentable. 
%Group 2 would be more likely to benefit from tDCS to mitigate higher anxiety levels. This information is still important because more effective anxiety interventions is highly desirable. Group 1 could receive tDCS at a lower priority, whereas Group 5 would not be recommended frontal-lobe tDCS. These results are immensely significant because they provide a proof of concept for using fMRI to provide revision to psychiatric guidelines for precision anxiety intervention. 

%to provide revision to the guidelines for precision psychiatrist of tDCS intervention. We found Groups 1 and 5 tend to be resistant to frontal-lobe tDCS; Groups 2 and 4 have evidence to be strongly recommended to have frontal-lobe tDCS for anxiety intervention. 

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